ASP-COMPLEX: redefining antimicrobial stewardship to improve outcomes in high-risk patients with severe infections.

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Learn more: PMC Disclaimer | PMC Copyright Notice Rev Esp Quimioter . 2026 May 22;39(3):191–237. doi: 10.37201/req/174.2026 Show available content in en es ASP-COMPLEX: redefining antimicrobial stewardship to improve outcomes in high-risk patients with severe infections Juan Pasquau 1, * , Francisco Javier Candel 2 , José Luis del Pozo 3 , Ángel Estella 4, 5, 6 , Carmen Hidalgo-Tenorio 1, 7, 8 , Jesús Ruiz-Ramos 9 , Juan González-del-Catillo 10 , Natalia Chueca 11 , Francisco López-Medrano 12, 13, 14, 15 , Esperanza Merino 16 , Francisco Anguita-Santos 17 , Carolina Garcia-Vidal 18 , Miguel Salavert-Lletí 19 Author information Article notes Copyright and License information 1 Servicio de Enfermedades Infecciosas, Hospital Universitario Virgen de las Nieves, Granada, España 2 Servicio de Microbiología Clínica, Hospital Clínico de San Carlos, Madrid, España 3 Servicio de Enfermedades Infecciosas y Microbiología Clínica, Clínica Universitaria de Navarra, Pamplona, España 4 Unidad de Cuidados Intensivos Hospital Universitario de Jerez, Jerez de la Frontera, Cádiz, España 5 Departamento de Medicina y Cirugía, Facultad de Medicina, Universidad de Cádiz, Cádiz, España 6 Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Cádiz, España 7 Instituto de Investigación Biosanitario de Granada (IBS-Granada), España 8 Departamento de Medicina, Universidad de Granada, Granada, España 9 Servicio de Farmacia, Hospital de la Santa Creu i Sant Pau, Barcelona, España 10 Servicio de Urgencias, Hospital Clínico de San Carlos, Madrid, España 11 Servicio de Microbiología Clínica, Hospital Clínico de San Cecilio, Granada, España 12 Unidad de Enfermedades Infecciosas, Hospital Universitario 12 de Octubre, Madrid, España 13 Instituto de Investigación Sanitaria Hospital 12 de Octubre (i+12), Madrid, España 14 Departamento de Medicina, Universidad Complutense de Madrid, Madrid, España 15 Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC; CB21/13/00009), Instituto de Salud Carlos III, Madrid, España 16 Servicio de Enfermedades Infecciosas, Hospital General Universitario de Alicante, Alicante, España 17 Servicio de Enfermedades Infecciosas, Hospital Clínico de San Cecilio, Granada, España 18 Servicio de Enfermedades Infecciosas, Hospital Clínic, Barcelona, España 19 Unidad de Enfermedades Infecciosas, Hospital Universitario de La Fe, Valencia, España Author contributions All authors have read and agreed to the published version of the manuscript. * Correspondence: Juan Pasquau. jpasquau@gmail.com Roles Juan Pasquau : Conceptualisation, Writing—original draft preparation Francisco Javier Candel : Writing—review and editing José Luis del Pozo : Writing—review and editing Ángel Estella : Writing—review and editing Carmen Hidalgo-Tenorio : Writing—review and editing Jesús Ruiz-Ramos : Writing—review and editing Juan González-del-Catillo : Writing—review and editing Natalia Chueca : Writing—review and editing Francisco López-Medrano : Writing—review and editing Esperanza Merino : Writing—review and editing Francisco Anguita-Santos : Writing—review and editing Carolina Garcia-Vidal : Writing—review and editing Miguel Salavert-Lletí : Writing—review and editing Received 2026 Apr 15; Accepted 2026 May 8; Issue date 2026. © 2026 The authors. This article is distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International license (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ ). Published with Index on behalf of Sociedad Española de Quimioterapia. PMC Copyright notice PMCID: PMC13231329  PMID: 42200305 Abstract At a time of great concern regarding trends in morbidity and mortality from infections, and mindful of the limitations of our current approaches to combating them, this document sets out a structured programme of proposals aimed at improving, in the short and medium term, the survival of the most at-risk patients with severe infections. These are complementary proposals which, on the one hand, reinforce already established initiatives and, on the other, introduce some novel aspects to the approach to this complex problem. They focus on accelerating, standardising, and refining the clinical diagnosis of severe infection and its microbiological diagnosis and, above all, on improving the effectiveness of antimicrobial treatment, based on its immediate initiation with the most effective treatment available, and with an improved capacity to integrate innovation into all stages and processes. This programme is designed to be implemented within the framework of the Antimicrobial Stewardship Programmes (ASPs) (hence the name ASP-COMPLEX), and requires appropriate dissemination and surveillance policies, alongside a collaborative environment among the multiple stakeholders involved in delivering innovation (experts, ASP teams, healthcare administration, academia, the pharmaceutical industry, etc.). Keywords: Antimicrobial stewardship programmes, Severe infection, At-risk patients, Antimicrobial resistance, Antimicrobial policy 1. Introduction The problem Mortality associated with infections constitutes one of the most significant public health challenges globally. It is estimated that infectious diseases contribute to approximately 20% of global mortality, with particularly high and sustained case-fatality rates in the context of severe infections and sepsis [ 1 ]. In recent decades, notable international awareness has developed regarding this problem, accompanied by multiple initiatives aimed at optimising antimicrobial use and curbing the threat of resistance [ 2 – 22 ]. However, despite these efforts, available evidence has not consistently demonstrated a significant reduction in mortality associated with severe infections [ 23 – 29 ]. This apparent mismatch suggests a need to critically review current approaches and explore new conceptual frameworks explicitly oriented towards reducing morbidity and mortality, both at the individual patient level and in terms of global health. The reasons why we have failed to substantially reduce mortality associated with infections, or to counteract increasingly alarming projections, are complex and multifactorial. Factors certainly include the continuous increase in at-risk patients—who are more prone to infection and to progression to severe disease and death—as well as the complexity of these infections and their clinical management, which diversifies and hinders adherence to standards of care and guidelines. This scenario places increasing pressure on healthcare systems, exposing structural and organisational limitations that directly impact clinical outcomes and threaten the sustainability of the healthcare systems. Another key factor is the development of antimicrobial resistance (AMR), which is directly linked to antimicrobial exposure and the selective ecological pressure it generates. AMR has substantially increased patient vulnerability, compromised microbiological diagnosis—making diagnostic delay a clear risk factor for mortality—and complicated the therapeutic management of infections due to the limited availability of effective options and higher rates of treatment failure, ultimately leading to a significant increase in morbidity and mortality [ 23 , 25 – 28 , 30 ]. Our response (The current model) Status analysis. Although there are many other factors that increase patient vulnerability, complicate clinical management, and compromise outcomes, AMR has emerged as one of the most visible and potentially actionable determinants of this problem. For decades, it has formed the core rationale for antimicrobial control policies and antimicrobial stewardship programmes (ASPs) [ 2 – 4 , 7 – 22 , 31 ]. In practice, and predominantly for more than 30 years, the main objective of many of these programmes has been the containment or reduction of antimicrobial consumption, through initiatives promoting the appropriate, rational, or prudent use of antibiotics. This approach explains why the key data used by ASPs, national plans (such as the PRAN in Spain), the European Centre for Disease Prevention and Control (ECDC), and the World Health Organization Global Action Plan (WHO GAP) are antimicrobial consumption (measured in DDD, DOT, and expenditure) and the rate or trend of infections caused by resistant bacteria. Consequently, current objectives—formulated in 2018 and projected through to 2030—prioritise a 20% reduction in global antibiotic consumption (27% in the case of the PRAN in Spain [ 32 ]) and promote the use of older, narrow-spectrum antimicrobials in at least two out of three prescriptions, according to the WHO Access, Watch and Reserve (AWaRe) classification [ 15 – 18 ]). These efforts aim to achieve a 3%—15% reduction in the incidence of certain severe infections caused by multidrug-resistant (MDR) bacteria by 2030. These are essentially preventive, ecologically driven objectives that seek first to minimise AMR, and subsequently, in the medium to long term, reduce the morbidity and mortality associated with infections. The overriding concern with the ‘ecological preservation’ of antimicrobial therapies often leads to restrictive usage policies, particularly regarding innovative products. These are frequently viewed as ‘endangered species’ that must be protected to maintain their future efficacy [ 33 ], enforced through external controls, validations, and rigorous prescribing criteria [ 34 – 36 ]. As a consequence, the needs of current patients with severe infections and high mortality risk—who require the most effective treatments—may be overlooked. To address this, more real-time attention should be devoted to identifying and correcting insufficient or suboptimal treatments, and to reducing the administrative and scientific barriers that limit access to intensified, personalised antibiotic therapies and innovation. Results. Although these policies have been in place and have enjoyed broad administrative support for over 30 years, recent data show that, between 2018 and 2023, the targets for reducing overall antibiotic consumption, the consumption rate of antibiotics in the Watch and Reserve group, and AMR-associated infections have not been met. On the contrary, the global incidence of these infections has increased significantly—by approximately 40% worldwide [ 29 ], with similar trends observed in Europe [ 37 – 39 ] and Spain [ 40 ]. The Organisation for Economic Co-operation and Development (OECD) predicts that infections caused by bacteria resistant to ‘last-resort antibiotics’ will double (with increases ranging from 50% to 300%) between 2005 and 2035 [ 41 – 42 ]. In Africa, mortality attributable to AMR now exceeds the combined death toll of tuberculosis, malaria, and HIV, despite receiving far less attention and investment [ 43 – 44 ]. Similarly, mortality and case-fatality rates for severe infections associated with AMR have shown no improvement [ 1 , 23 – 27 , 45 ]. Real-world data from Spain [ 28 ] place these figures significantly higher than the international estimates, such as those from the WHO, which identify AMR as the leading cause of death by 2050 [ 10 ]. Furthermore, robust predictive models suggest that, without a strategic pivot, mortality (attributable and associated) will enter a phase of continuous increase up to 2050 [ 27 ]. This scenario has ultimately prompted a reassessment of the problem on the international political agenda. In September 2024, the political declaration of the second UN General Assembly High-Level Meeting on AMR, and in May 2025 at the 78th World Health Assembly, established a clear global commitment to reduce AMR-associated mortality [ 46 – 47 ]. To this end, the 10-20-30 Agenda was finalised. This agenda aims to achieve, by 2030 relative to 2019, a 30% reduction in the inappropriate use of antibiotics in animals, a 20% reduction in humans, and, for the first time, a specific target of a 10% reduction in mortality [ 44 , 46 , 47 ]. Achieving this seems highly unlikely without fundamental conceptual, structural, and cultural changes to our current strategies [ 27 – 28 ]. Conclusion. Consistently available data indicate that our response to this challenge has not delivered the expected outcomes, and that our current model for combating severe infections and AMR has become disconnected from clinical outcomes. This realisation demands a strategic review to realign our efforts towards improving clinical outcomes and driving down mortality. We require a reframing and reconfiguration of the existing model to generate, complement, and reinforce concrete, operational initiatives that can impact clinical outcomes and improve the real-world prognosis of these infections in the short to medium term. 2. Review and reconfiguration of the diagnosis and treatment of severe infections To bring about real operational changes in how we approach severe infections and their high associated mortality, it is essential to: A. Recognise and address the limitations of current antibiotic policy Conceptual limitations Current antibiotic policies present at least two significant conceptual issues: The excess mortality associated with infections is primarily and predominantly due to the problem of AMR. It is vital to acknowledge that many other factors influence mortality besides resistance—including biological vulnerability, as well as social, structural, and care-delivery factors. These can exert an even greater impact than AMR itself and warrant increased attention [ 27 , 41 – 43 ]. This involves better, broader, and earlier recognition of severe infections, their specific aetiology, and their complex physiopathology (resistance, virulence, persistence, etc.), alongside the pursuit of more individualised and effective treatments. AMR is primarily caused by the excessive and inappropriate use of antimicrobials [ 8 , 10 – 12 , 16 , 18 , 22 , 32 – 33 , 38 , 48 – 51 ]. AMR is a biological phenomenon of such complexity [ 52 – 55 ] that relying solely on prudent and appropriate use to control it may prove insufficient. Inappropriate antibiotic use remains a key, modifiable driver of resistance. However, trying to strictly define, validate, standardise, and enforce ‘appropriate’ or ‘rational’ antibiotic use is practically unattainable given the immense complexity and variability of managing severe infections in real clinical settings [ 56 – 57 ]. Furthermore, other independent factors influence the development of resistance—including necessary, appropriate exposure to antimicrobials—meaning that even the most perfect prescribing controls would ultimately be insufficient to halt AMR. As early as 2008, the Infectious Diseases Society of America (IDSA) noted that: “it is commonly expressed that physician misuse of antibiotics is the cause of antibiotic resistance in microbes, and that if we could only convince physicians to use antibiotics responsibly, we could win the war against microbes. Unfortunately, this belief is a fallacy that reflects an alarming lack of respect for the incredible power of microbes” [ 58 ]. We must accept that the true driver of AMR is the extraordinary adaptive capacity of microbes, which has evolved over more than three billion years in a continuous struggle for survival, allowing them to evade virtually any antimicrobial product or strategy through a wide array of mechanisms—often even before direct exposure [ 58 – 60 ]. Evidence shows that resistance is deeply embedded in the bacterial genome and can emerge and persist independently of antimicrobial exposure [ 59 , 61 – 66 ]. This does not only occur randomly or de novo ; it is often driven by natural exposure to hostile environments and biological compounds (such as host immune responses or natural antimicrobial peptides). Such encounters exert selective pressure that promotes adaptive mechanisms, which can then confer cross-resistance to synthetic antibiotics without any prior exposure [ 58 – 59 , 61 , 67 ]. Furthermore, resistance genes can disseminate horizontally even in the absence of antibiotic pressure [ 68 – 71 ]. Moreover, as long as antibiotics remain essential to treat infections, any use of these agents —including appropriate and necessary therapy— will continue to serve as a driver of the development of resistance [ 59 ]. This is further complicated by phenomena like ‘genetic capitalism’ [ 72 ], where bacteria already carrying multiple resistance genes acquire and accumulate additional genetic elements. This process frequently occurs in severe clinical settings where complex, appropriate therapies are required, resulting in higher levels of AMR, cross-resistance, co-resistance, and the unmasking of cryptic resistance. These dynamics drive the continuous expansion of the microbial resistome [ 29 , 45 , 73 ], extending far beyond the issue of inappropriate antibiotic use [ 58 – 72 ]. From this perspective, controlling antibiotic use, while necessary and clinically relevant in many local contexts, is inherently insufficient as a standalone strategy to contain AMR. In a world dominated by microbes with superior evolutionary adaptability, our goal should not be to win an impossible battle (such as eradicating pathogens or achieving a perfect ecological balance via ‘appropriate’ use), but rather to minimise the negative consequences of their adaptation [ 59 ]. As the IDSA succinctly stated: “Here, the resource that must be protected and restored is antibiotic effectiveness” [ 58 ]. In practical terms, and to give an example from clinical practice, the focus should be more on the clinical efficacy of antibiotics than on their environmental impact [ 59 ]. It entails focusing more on the ‘last-line’ antibiotic that was withheld despite being clinically indicated, than on the antibiotic that was unnecessarily prescribed—provided the latter was effective and did not directly cause clinical harm. This perspective should not be interpreted as a dismissal of ecological stewardship principles, but rather as an attempt to dynamically balance individual patient benefit with long-term ecological responsibility in particularly high-risk scenarios. If our approach over the last 75 years has not been sufficiently successful, we must refine it, pursuing continuous improvement to close the gap between human innovation and the evolutionary advantage of the microbial genome [ 9 , 58 – 59 , 74 ]. Methodological limitations In their attempt to reduce variability in clinical practice and control costs, our antibiotic policies harbour methodological problems that may hinder, in the clinical setting and in real time, therapeutic precision, individualisation, and the transfer of the benefits of innovation to the patients who need them most. Examples include: Inefficiency of academic training. Training clinicians to manage complex infections effectively is challenging [ 75 , 76 ]. Traditional educational programmes are voluntary, highly dependent on teaching quality, often lack clinical context, and tend to oversimplify management. They typically focus on protocolisation, reducing clinical variability, preventing AMR, and achieving cost efficiency [ 8 , 16 , 18 , 22 , 32 – 33 , 38 , 48 – 51 , 77 ]. Consequently, the urgent needs of at-risk patients with severe infections are often omitted from this training framework. Digital solutions, advanced software, and artificial intelligence (AI) tools capable of providing structured, real-time clinical decision support systems (CDSS) offer promising ways to overcome these limitations [ 78 ]. Over-reliance on the highest-level scientific evidence when formulating recommendations, protocols and guidelines. While evidence-based medicine is fundamental, insisting on the highest-level evidence (such as large meta-analyses) to guide every prescription can result in suboptimal care for the most at-risk patients [ 16 , 18 , 33 – 34 , 79 – 83 ]. The methodologies used to generate this evidence are often designed for chronic conditions rather than acute, severe infections. As a result, genuine clinical benefits in critical and complex scenarios may not meet standard evidence thresholds and are excluded from clinical guidelines (e.g., non-inferiority studies, which are accessible but provide little impetus for adopting therapeutic innovations) [ 79 – 85 ]. Optimising the management of these infections requires a more adaptive, less dogmatic approach that incorporates diverse evidence sources, including observational and real-world data, structured clinical experience, and expert consensus. Outsourcing of governance and control over prescribing. Antimicrobial treatments are often subject to strict control by external departments such as pharmacy, microbiology, preventive medicine, infectious diseases committees, ASPs and health administration, which regulate acquisition and dispensing based on centralised therapeutic positioning reports and adherence to rigid guidelines. While these controls aim to prevent AMR and contain costs, they often favour highly restrictive practices for the most innovative and expensive agents. This can introduce delays and inflexibility in patients with severe infections, where early, individualised therapy directly determines prognosis [ 4 , 84 – 91 ]. Properly structured stewardship teams should instead facilitate the positioning and delivery of innovative pharmacological, diagnostic, and clinical resources to these critical patients. Ethical limitations Protecting and preserving the long-term effectiveness of antibiotics is a core ethical duty towards future patients. However, in severe infections affecting highly vulnerable individuals, a fundamental ethical tension arises between the immediate benefit to the individual and long-term collective responsibility. This conflict cannot be resolved solely through general policies or isolated individual decisions; it requires an integrated approach that balances the specific clinical context with population-level impacts. The legitimacy of limiting potential benefits for a current patient to preserve future utility depends on clinical severity, diagnostic uncertainty, and the availability of effective therapeutic alternatives. While the future benefits of antibiotic containment may be contingent and dependent on broader scientific progress, they remain a valid consideration within a framework of intergenerational equity. The ethical challenge is therefore to find a dynamic balance that maximises immediate clinical benefit without disproportionately compromising the future sustainability of antimicrobial therapies [ 87 , 92 ]. Philosophical limitations The fact that a particular viewpoint has become the ‘official view’ and remains widely supported for many years does not mean that it is always the most correct or effective solution for complex problems. Self-criticism requires acknowledging that knowledge management is often subject to cognitive biases that entrench intellectually attractive positions, even when they diverge from real-world outcomes [ 93 ]. The primary focus on preventing AMR solely through the ‘appropriate use of antibiotics’ is a clear example of anchoring heuristic—a cognitive bias whereby we anchor ourselves to a compelling initial solution and limit our search for better alternatives. This anchoring explains why institutions continue to measure antibiotic consumption and resistance rates—which are familiar and relatively easy to track—rather than monitoring real-world clinical outcomes and mortality, which are more complex to capture but represent the ultimate metric [ 51 , 94 ]. B. Recognise the need to refocus and revisit the current antibiotic policy The strategy should be focused on an ASP aimed at severe infections in the most at-risk patients and centred on therapeutic effectiveness. ASPs have driven major progress in optimising antimicrobial use and raising awareness of AMR. However, given the limitations outlined above, they have not consistently improved clinical outcomes, which should remain their foremost objective. A strategic adjustment is therefore needed. This does not mean replacing current ASP models, many of which already incorporate several of these principles, but rather expanding stewardship towards a more outcome-oriented and effectiveness-driven framework in selected high-risk scenarios. This reconfiguration is built upon three primary pillars: Target population: Focussed on high-risk, at-risk patients with severe infections that are ‘difficult to treat’. These are the patients who experience the highest mortality rates and for whom restrictive prescribing policies are often least successful. Although vulnerability to infectious diseases is a very broad, complex and difficult-to-define concept, encompassing genetic, biological, psychological, social, administrative and political factors that are difficult to characterise and apply [ 27 , 95 – 97 ], in this document, for operational reasons, we define vulnerability on the basis of clinical and biological criteria that are well-validated in the healthcare setting, with a demonstrated capacity to severely impair the patient’s immune response to infection and/or to hinder or delay access to effective treatment, thereby increasing the risk of disease progression and mortality [ 98 – 101 ] ( Figure 1 ). Thus, the main factors contributing to vulnerability would be: a) clinical or pharmacological immunosuppression (of varying intensity depending on aetiology, duration of illness, treatment, net state of immunosuppression, etc.); b) Immune compromise or immune dysfunction occurring in the critically ill patient (due to immunoparalysis associated with the severity of the condition or the imbalance between immune exhaustion and the hyperinflammatory phases of shock), in patients with severe, multiple or decompensated comorbidities (as these significantly reduce ‘functional reserve’), or in geriatric frailty [ 100 , 102 – 106 ]; c) the presence of prosthetic/invasive devices (which, through biofilm, create ‘sanctuaries’ that are very difficult for antibiotics to access, and which impair the defensive capacity of the ‘invaded’ organs); and d) colonisation by AMR-bearing microbes, the risk of which is increased in the conditions described above, but above all in patients with recent (within 6 months) exposure to antimicrobials or to the healthcare system (especially if there are problems with AMR endemicity) [ 107 – 112 ]. Although a precise, validated and reproducible definition of vulnerability was critical to our aims, the fact is that, given the need to protect these patients in the short term, we have adopted a pragmatic definition—setting aside the academic rigour required for research—under which we consider a patient to be vulnerable if they meet any of the criteria listed above. We believe that there remain more issues of sensitivity than of specificity in the operational definition of vulnerability commonly used in clinical practice; therefore, we believe that, rather than hindering its application with proposals for rigid, validated scales or ‘scores’, it makes sense to liberalise it, in order to facilitate the attribution of vulnerability. Primary objective: To maximise the effectiveness of antimicrobial treatment. In the context of realigning antibiotic policy with improved clinical outcomes, priority should be given to achieving therapeutic effectiveness, understood as maximising the likelihood of rapid microbial eradication and early clinical control, which have a direct impact on mortality. This is achieved by taking due account of the probable aetiology, the patient’s characteristics, the clinical syndrome and the site of infection, as well as the pharmacokinetics/pharmacodynamics (PK/PD) of the antibiotic treatment. Figure 1. Clinical-biological factors influencing vulnerability to infections. Open in a new tab MDR: multidrug resistance; PK/PD: pharmacokinetics/pharmacodynamics; MOF: multiple organ failure; TDM: therapeutic drug monitoring; RRT: Renal replacement therapy; cSSTI: complicated skin and soft tissue infection; cIAI: complicated intra-abdominal infection; CVC: central venous catheter; UC: urinary catheter; IS: immunosuppression; LC: liver cirrhosis; ECMO: Extracorporeal Membrane Oxygenation. In practical terms, this implies: Encouraging prescribers to adopt a decisive commitment to providing, for each individual patient, the antibiotic treatment with the highest degree of anticipated effectiveness available. In this challenging prescribing scenario, what is appropriate is what is effective (beyond ecological impact, previous conventions regarding appropriate or adequate therapy, efficacy in the Petri dish, and the recommendations of guidelines). The promotion of initiatives and proposals that facilitate the prescribing of effective treatments. This is the critical point of the entire programme: the conviction—which we share with recent institutional proposals [ 42 , 113 – 115 ]—that, in the face of a severe, ‘difficult-to-treat’ infection in the target population we have defined, decision-making must prioritise immediate clinical effectiveness, whilst always maintaining a dynamic balance with the need to minimise the ecological impact. ASPs and antibiotic policies that place greater emphasis on restrictive programmes for the prevention and reduction of AMR development and costs achieve variable and inconsistent results regarding the evolution of AMR [ 116 ] and, above all, regarding the improvement of the most important clinical outcomes [ 4 , 86 , 94 , 117 – 119 ]. An inevitable consequence of this approach is the need to assess and monitor mortality as the primary indicator of effectiveness in any setting (ASP) treating this type of infection and these ‘difficult-to-treat’ patients. It is also important to monitor aspects related to morbidity, including complications, sequelae, readmissions, adverse effects, drug interactions, etc. Operational management framework: ASP and the ASP-COMPLEX concept. We refer to this proposal as ‘ASP-COMPLEX’ because, although it consists of a set of recommendations aimed at prescribers to facilitate (and restore) therapeutic effectiveness, we understand that they can only be implemented from a multidisciplinary, expert, realistic and adaptable perspective linked to the healthcare administration of ASP’s. The ‘ASP-Complex’ approach involves reviewing the clinical care pathway for this type of infection, seeking out and identifying obstacles that may be blocking or slowing down access to the effectiveness required by the complex approach. These obstacles lie not only in AMR and biological vulnerability, but also in many other care deficits that have a major impact on prognosis [ 27 ]. The next step is to turn obstacles into opportunities for improvement, and thus to generate and promote, here and now, as soon as possible, specific initiatives that have been shown to have a positive impact on patient outcomes, specifically targeting each of the obstacles or problems identified. These initiatives must be based on the continuous improvement of existing resources and on better and wider access to innovation. Finally, they must be translated into concrete, measurable and monitorable operational objectives (surveillance), which can be implemented (dissemination), and are capable of demonstrating their impact on clinical outcomes. Lastly, and very importantly, it is worth highlighting the significance, within this improvement process, of the proper establishment of strategic alliances between all stakeholders involved in the healthcare system (academia, the pharmaceutical industry, government and healthcare professionals). The available evidence indicates that processes of this type, such as the approach to HIV, tuberculosis, candidemia bundle or some ASPs, can be associated with tangible clinical benefits, which reinforces the viability and necessity of this strategic refocusing. In summary , we believe that, in this context (at-risk patients with severe infections), it is necessary to take a step further and move from the conceptual framework to the operational level, where decisions are made in real time and their consequences are immediate. This document is therefore conceived as a proposal deliberately geared towards clinical practice, designed to address the complexity inherent in severe infections in these at-risk patients. This is not about adding new layers of regulation, but rather about precisely identifying the critical points where therapeutic effectiveness is gained or lost. From here, the focus shifts to early diagnosis (clinical and microbiological), risk stratification, and the selection of the treatment most likely to have a clinical impact. In the following sections, the reader will find a structured elaboration of these elements, with the aim of facilitating faster decisions, based on sound scientific grounds, and more closely aligned with the actual reduction of morbidity and mortality. This implies accepting that, in this scenario, effectiveness is not an option, but a clinical imperative. 3. ASP-COMPLEX: an ASP aimed at improving the effectiveness of infection treatment With the aim of proposing initiatives capable of improving the prognosis of severe infections, we have followed a process based on intellectual and scientific reflection, the analysis of our experience, and consensus-building among panel experts, including the ASP-COMPLEX course and forum held at the SEIMC Satellite Campus in May-June 2025. This has led to the development of 15 proposals targeted at three critical decision areas in the management of severe infections in at-risk patients ( Figure 2 ): Figure 2. Open in a new tab Overview of 15 proposals for managing better severe infections in high-risk patients The initial phase: focused on the clinical diagnosis of severe infection and the establishment of a microbiological diagnosis. The point of prescription: when antimicrobial treatment is prescribed and its degree of efficacy is determined. Programme dissemination and surveillance: initiatives to make the programme operational and to monitor its impact on clinical outcomes. A. Proposals to improve clinical and microbiological diagnosis of severe infection In the clinical management of infections, the primary objectives are to assess severity, which stratifies the risk of morbidity and mortality and facilitates rapid access to empirical treatment; to identify the source of infection, which facilitates more targeted empirical treatment and better control of the infectious process; and to determine the microbiological aetiology, which allows for the appropriate antimicrobial treatment. In at-risk patients, delays or inaccuracies in recognising a serious infection, its microbiological aetiology, and the septic focus have a significant impact on prognosis. Therefore, these constitute critical points for optimisation: A1. Achieving earlier and more widespread diagnosis of severe infection Recent consensus documents in the field of emergency medicine have emphasised that, despite advances, the early recognition and optimisation of the initial management of severe infection remain significant areas for improvement in clinical practice [ 120 ]. The sensitivity of the best-validated current protocols and guidelines for diagnosing severe infection is poor, particularly in the most at-risk patients and those at high risk of harbouring AMR [ 120 – 126 ]. The combined use of clinical scales and biomarkers has proven useful in identifying severe infection, although with limitations in sensitivity and applicability in certain patient subgroups [ 127 ]. Our proposals to achieve an earlier and more universal diagnosis of severe infection are: Stratify severity using a multimodal approach based on the patient’s degree of vulnerability, clinical features and biomarkers : Incorporate an appropriate assessment and categorisation of vulnerability into all diagnostic protocols for sepsis/severe infection [ 27 , 100 – 101 , 122 – 123 , 127 ]. Lower the clinical diagnostic threshold in these at-risk patients. The presence of any vulnerability criterion in a patient with suspected infection and a SOFA score < 2 would necessitate continued monitoring and the search for other criteria for severe infection which, even at the cost of a loss of specificity, are more sensitive, such as SIRS, NEWS, NEWS-2, MEWS, critical elevations in biomarkers [ 128 – 131 ], predictors of bacteraemia [ 132 ], AI, etc.) [ 120 – 126 , 132 – 134 ]. Incorporate new biomarkers of severe infection, such as PCT, MDW, MR-proADM; the latter two may be useful in elderly patients for the early detection of severe infections and risk stratification [ 134 – 136 ]. Consider the incorporation of new technologies based on big data/machine learning/ artificial intelligence. Moving beyond the traditional approach based on the development and application of clinical scales and biomarkers, and facilitating automation, accuracy and independence from training, the development of AI-based algorithms has shown potential for improving the early detection of severe infection and optimising clinical decision-making in real time [ 137 – 141 ]. A2. Identifying and controlling the source of infection more rapidly Identifying the source of infection facilitates a more targeted empirical treatment using a syndromic approach, and sets a time limit for optimising control of the focus—apart from antibiotic therapy—when it requires additional measures (drainage or instrumental or surgical debridement, replacement or removal of catheters, tubes, prosthetic material, etc.), which, if delayed beyond the first 6 hours after diagnosis, has been shown to worsen the prognosis [ 142 – 145 ]. A3. Achieving an earlier, more comprehensive and accurate microbiological diagnosis ‘Etiological uncertainty’ increases the risk of providing inappropriate treatment; and, consequently, in vulnerable and critically ill patients, the risk of mortality [ 125 – 126 ]. To optimise microbiological diagnosis, we propose: Fully integrating Microbiology Departments into clinical decision-making, acting as strategic partners and not merely as data providers. This requires that hospitals treating patients with sepsis consider continuous and comprehensive 24/7 care coverage as standard, something that is still far from reality (only 10% of European centres have a continuous microbiology service, although they have 80% adherence to other sepsis care processes) [ 146 ]. Incorporating the PRODIM concept (Programmes for the Optimisation of Microbiological Diagnosis) to maximise the clinical value of microbiological diagnosis [ 147 ]. It is not just a question of which test we use, but of for whom, when and why we use it. This represents the adaptation of microbiology to clinical needs and health outcomes, prioritising the actual impact on the patient and their prognosis over technical metrics. In this multidisciplinary context, selecting initiatives that can improve the effectiveness of infection treatment in the short term , such as the following: Incorporate new microbiological techniques for rapid, accurate and comprehensive diagnosis . Conventional techniques, which remain the gold standard for epidemiological diagnosis and surveillance, have significant limitations in at-risk patients (primarily the delay inherent in microbial growth, but also sensitivity that is highly dependent on the source and quality of the sample and on prior antibiotic therapy, etc.) [ 148 – 150 ]. The diagnostic yield of blood culture is 10–20%, and the development of rapid diagnostic techniques has accelerated and improved this to 60–70%, presenting a critical opportunity for optimisation [ 123 ]. Syndromic molecular platforms, such as those used in severe respiratory infections [ 151 – 153 ], central nervous system (CNS) infections [ 154 – 157 ], or severe sepsis [ 152 , 158 ], allow the simultaneous detection of multiple pathogens and, in some cases, resistance genes, directly from clinical samples or positive blood cultures, with very short turnaround times [ 152 , 158 ], with high analytical sensitivity, enabling the detection of slow-growing or difficult-to-culture pathogens, non-viable microorganisms following prior antibiotic therapy or due to nutritional or atmospheric requirements, intermittent or low-load bacteraemia, and co-infections that might go undetected with conventional techniques [ 151 – 152 , 159 ]. Furthermore, when integrated into well-structured and standardised diagnostic processes, they reduce interobserver variability and facilitate their implementation in settings where expert personnel are not always available. Rapid molecular genomic or proteomic techniques for identification (MALDI-TOF, syndromic panels with multiple gene targets) and phenotypic antibiotic susceptibility testing (AST) (using immunochromatography, GeneXpert, etc.) based on positive blood cultures [ 160 – 163 ], have demonstrated a reduction in time to appropriate treatment, lower exposure to broad-spectrum antibiotics, facilitation and justification of treatment escalation or de-escalation, and reduced mortality in some cases, particularly in vulnerable patients [ 160 – 161 , 164 – 166 ]. Metagenomics and emerging techniques applied to clinical samples have shown promising results in selected settings, enabling the detection of unsuspected pathogens and emerging resistances; however, their availability is limited, their interpretation is complex, and their clinical impact is still under evaluation, so they should currently be reserved for selected critically ill patients [ 167 – 169 ]. However, these advantages are accompanied by significant limitations, both in terms of sensitivity (for example, bacteraemia and fungaemia platforms such as Biofire ® BCDI2 Panel from bioMérieux ® rely on a prior positive blood culture result, which limits their impact to approximately 40–50% of sepsis episodes [ 149 ]); and in the interpretation of resistance mechanisms, as the absence of detection does not rule out relevant mechanisms not included in the panel, such as AmpC-mediated carbapenem resistance and impermeability in Pseudomonas aeruginosa [ 170 ]), as well as specificity (in non-sterile samples, such as respiratory samples, they may over-detect colonisation, particularly in patients on prolonged mechanical ventilation [ 159 ]; or also in the interpretation of resistance genes, the detection of a gene does not guarantee its phenotypic expression [ 170 ]), of spectrum, as there is heterogeneity in the targets available in each panel, and results must be assessed bearing in mind that there will always be potential targets not included in the panel we use), or in relation to efficiency (their high costs and the complexity of their interpretation necessitate continuous monitoring of the quality of their use – the key is not to have more tests, but to know for whom, when and how to apply them, which requires constant training and communication – and this must always be carried out within diagnostic stewardship programmes [ 147 , 158 , 171 – 175 ], and, for now, focused on at-risk patients for whom early information may influence clinical management and outcomes). Optimise the local microbiological profile (the cumulative susceptibility). Traditional mapping has significant limitations, and in order to turn it into a useful tool for improving empirical aetiological coverage to >90–95%, more and better assessment and information would need to be incorporated on aspects such as the following: stratification by hospital areas/departments, source of infection, patient type and vulnerability, and clinical syndromes; on the relative frequency of microorganisms in each syndrome, polymicrobial infections, epidemic outbreaks (in real time), interpretation of the MIC focused on its impact on clinical outcomes, resistance mechanisms, the relationship between weighted incidence and susceptibility; and, very importantly, on new antibiotics and combination regimens. New techniques combining clinical and microbiological data (Weighted-Incidence Syndromic Combination Antibiogram -WISCA) would help us predict the likelihood of coverage by a specific antibiotic therapy (using one or more antibiotics) for a specific infection [ 176 ]. Such an optimised map would be very useful for substantiating and configuring local antimicrobial treatment guidelines in real time. Place greater focus on the post-identification phase of microbiological diagnosis , with the aim of better determining, on an individual basis and in real time, the most effective antibiotic treatment for isolated bacteria exhibiting the most severe resistance phenotypes. There are new microbiological aspects which, although still in the future, could be considered within the context of this ASP-COMPLEX, such as monitoring changes in bacterial load and controlling the inoculum, the emergence of AMR during treatment, etc. B. Proposals for more effective prescribing of antimicrobial therapy The primary objective of this document is to promote therapeutic effectiveness—that is, prescribing treatments with the highest degree of anticipated effectiveness available for at-risk patients with severe infections. This is because the absence, delay, confusion, or complacency in the intention to design effective treatments is very common and has a significant impact on prognosis. In operational terms, therefore, the goal is to achieve a commitment to effectiveness at all levels that influence and contextualize decision-making regarding antimicrobial treatment: from the prescriber’s perspective to health administration, including expert institutional support (ASPs, guidelines, surveillance, etc) and training and research [ 177 ]. To this end, we propose measures that have demonstrated their ability to improve clinical outcomes, as shown in Table 1 . Table 1. Factors influencing therapeutic efficacy in infections Domain Variable Expected clinical impact Practical implication Host Immunosuppression, frailty, ARC, RRT/ECMO Increased risk of severity Extreme variability in drug exposure Risk of underdosing or toxicity Enhanced severity detection Accelerated and intensified therapeutic response Use of TDM, adjustments for dynamic renal and hepatic function, and loading doses Pathogen AMR, High inoculum, Persistence, Biofilm, Virulence Elevated MIC Therapeutic failure due to lack of bacterial eradication Selection of more effective therapies Optimization of PK/PD indices ( f AUC/MIC) Therapies targeted at biofilm, persistence, and virulence Source Lung, CNS, Endocardium, Abdomen, Urinary tract, Prosthetic material Varying degrees of drug penetration depending on tissues Formation of infectious sanctuaries Drug selection based on tissue diffusion High doses to overcome barriers (e.g., blood-brain barrier) and source control (surgery) Treatment Onset, coverage, PK/PD, combination therapy, toxicity Delayed onset increases mortality Sub-exposure promotes the emergence of resistance Early initiation of empirical therapy with maximum coverage (>90-95%) and efficacy PK/PD optimization Response assessment at 48 h (for adjustment, de-escalation, or rescue, and duration planning) System Drug access, rapid and precise microbiology, ASP, nursing Logistic or diagnostic delays invalidate the best pharmacological strategy Implementation of rapid microbiological diagnostics (MALDI-TOF, PCR…) Standardised administration protocols and support from ASP teams Open in a new tab ARC: Augmented Renal Clearance; RRT: Renal Replacement Therapy; ECMO: Extracorporeal Membrane Oxygenation; TDM: Therapeutic Drug Monitoring; MIC: Minimum Inhibitory Concentration; AMR: Antimicrobial Resistance. B4. Immediate initiation of empirical antibiotic therapy Delays in starting treatment following the diagnosis of a severe infection are critically important, as they significantly and rapidly worsen the prognosis, and, furthermore, are very common [ 121 , 123 , 178 – 186 ]. For this reason, the immediate initiation of antibiotic therapy in these patients must be an absolute priority, which must be implemented and maintained at all times. Although the point from which the time to the start of antibiotic treatment should be measured is the onset of the infection and its symptoms, in practice this must be shifted to the time at which the patient first contacts the health services. Even though this introduces an obvious bias, the accumulated evidence shows that in severe infections, the speed of treatment initiation, as a continuous variable, leads to significant benefits in terms of survival [ 121 , 123 , 178 , 180 – 185 ]. Thus, in the case of bacterial infections, current evidence suggests that initial or empirical antibiotic therapy should be started: within 1 hour if there is septic shock or definite or probable sepsis; or within 3 hours if sepsis is possible and cannot be ruled out within that time [ 121 , 123 , 178 , 180 , 184 ]. In special populations, such as oncohaematological patients with febrile neutropenia, a time threshold of as little as 30 minutes has even been established for the initiation of effective antibiotic treatment [ 187 ]. In addition to prescribing empirical antibiotic therapy early, it is advisable to monitor and confirm its correct administration, both timely and appropriate, by nursing staff. The same applies to acute and severe viral infections, albeit with different timing. For example, in influenza (and recently in SARS-CoV-2 infection), although it is well established that initiating antiviral treatment in at-risk patients within the first 48 hours of symptom onset reduces morbidity and mortality, this is a very uncommon practice [ 186 ]. B5. Selection of an appropriate, active and effective empirical antibiotic therapy The rate of inappropriate treatment, defined as prescription of a treatment that is inactive or ineffective against the causative agent, in the management of severe infections is very high (15–50%), particularly if the infection is caused by MDR bacteria (>40%) [ 80 , 84 – 85 , 183 , 188 – 205 ]; and this occurs in the most difficult-to-treat infections, even when guidelines are followed in 90% of cases (87% [ 84 ] – 92% [ 85 ]). Furthermore, this is a problem that is not improving over time [ 85 , 183 , 188 , 197 , 199 , 201 , 203 – 205 ]. In these infections, ineffective empirical treatment is associated with a significant increase in mortality [ 80 , 84 – 85 , 183 , 188 – 197 , 199 – 205 ]. Thus, it is imperative to improve the coverage of empirical treatment that we advocate in routine clinical practice, which should be above 90–95% in this patient group. This can only be achieved by providing, in guidelines and expert recommendations, treatments with a better PK/PD profile (i.e. better antibiotics, strategies and combinations), guidance on empirical treatment based on recent surveillance cultures of rectal, oropharyngeal or mucocutaneous swabs, previous colonisations or infection; and AI-based predictive tools such as clinical decision-making and antibiotic selection support systems, which have been shown to achieve greater therapeutic efficacy, a reduction in complications and significant decreases in mortality [ 85 , 188 , 200 – 206 ]. From efficacy to effectiveness In patients with severe infection and high vulnerability, the quality of empirical prescribing should not be judged solely on its probability of microbiological coverage, but rather on its ability to elicit an early, robust and effective therapeutic response in a biologically fragile host. In this context, the effectiveness of antibiotic therapy encompasses at least the following dimensions [ 207 – 214 ]: 1) spectrum or coverage of the likely pathogen; 2) intrinsic bactericidal capacity, derived from its PK/PD profile; 3) optimal timing and speed of initiation; 4) sufficient PK/PD exposure at the site of infection; 5) bactericidal activity in scenarios involving high inoculum, resistance, tolerance, persistence or high virulence, or an altered immune context in the host; 6) complementarity in the use of combinations – which determines the additive and synergistic effects of the combinations; and 7) feasibility of maintaining an effective, safe and sustainable therapeutic strategy during the first 48–72 hours. This perspective allows us to move beyond a binary interpretation of antibiotic therapy as ‘active’ or ‘inactive’ and shifts clinical reasoning towards a scale of probability of therapeutic success and effectiveness ( Figure 3 ). Figure 3. Open in a new tab From efficiency to effectiveness, from appropriate to optimal. Adapted from Ramasco et al. [ 177 ] And furthermore, it would serve to explain, at least in part and aside from critical vulnerability, the mortality that occurs in patients receiving theoretically appropriate antibiotic therapy (active against the causative microbial agent) [ 215 ]. This is a form of mortality that is likely to be common, yet relatively unnoticed and insidious, stemming from the difficulty in recognising and acknowledging the existence of this gap between efficacy and effectiveness. There is a substantial body of literature, including high-quality evidence [ 216 – 219 ], that illustrates this problem. Thus, the aim is to understand that each antibiotic exhibits these characteristics to varying degrees, generating different levels of efficacy that are distributed across a hierarchical, stratified scale. Consequently, among the antibiotics active against the causative agent, some are better or more beneficial than others for curing a specific infection in a specific patient and at a specific stage of the infection, such as hyperinflammatory vs immunoparalysis; microbiologically quiescent vs active rapid-growing, haematogenous vs compartmentalised, etc. [ 216 – 219 ]. Therefore, in this scenario, given the need to move from efficacy towards effectiveness and to ask ourselves how to achieve an effective treatment or the most active one possible from among those available for this type of patient, our proposals to improve effectiveness are as follows: B6. Selecting the best possible options from among the available antimicrobials In general, the best possible options for achieving effectiveness in the most difficult and demanding cases are the new antibiotics, as they are the result of innovation and a design focused on continuous improvement. Consequently, compared to their predecessors, they tend to have more favourable PK/PD profiles, which, by enhancing their bactericidal and microbial eradication capabilities whilst minimising their ecological impact and toxicity, confer clinical advantages that have been demonstrated in certain difficult or complex scenarios ( Figure 4 ) [ 213 , 216 – 217 , 220 – 357 ]. Figure 4. Open in a new tab Theoretical advantages of new vs. classic antibiotics Although the scientific evidence demonstrating their potential benefits is extensive and robust, access to this evidence is generally not straightforward. They are not prioritised either in research—because they require studies that are difficult to conduct—or in training—because they are applicable only to a select group of patients and are at odds with the predominant ecological and cost-containment objectives. However, the lack of top-quality evidence should not prevent access to a wealth of other scientific knowledge that helps us not only to guide but also to better prioritise the varying degrees of effectiveness of antibiotics across the multiple clinical scenarios encountered in clinical practice. Under comparable conditions and in specific scenarios, some of these studies suggest that certain groups of antibiotics are relatively more effective than others. Examples of a sufficiently well-established hierarchy, albeit with varying degrees of strength, are shown in Table 2 . Table 2. Comparative overview of new vs. classic antibiotics Hierarchical scale of effectiveness Microorganism / Scenario Cloxacillin > Vancomycin MSSA [ 220 – 223 ] Daptomycin > Vancomycin MRSA/MSSA [ 224 – 228 ] Oritavancin (and dalbavancin) > Daptomycin > Delafloxacin > Cefazolin/Cloxacillin > Vancomycin > Tigecycline and Linezolid S. aureus [ 220 , 229 – 234 ] * Ceftaroline > Ceftriaxone S. pneumoniae [ 235 – 240 ] Fluoroquinolones > third- and fourth-generation cephalosporins > piperacillin/tazobactam In the absence of resistance to fluoroquinolones, Enterobacterales and P. aeruginosa [ 213 , 241 – 244 ] * Carbapenems > 3rd/4th generation cephalosporins > Piperacillin/tazobactam Enterobacterales [ 216 – 217 , 245 – 254 ] (Not applicable if urinary tract infection caused by Enterobacteria with ESBL) Ceftolozane-Tazobactam > Meropenem > Ceftazidime-Avibactam > Ciprofloxacin > Ceftazidime/Cefepime > Piperacillin-Tazobactam P. aeruginosa [ 255 – 261 ] * Carbapenems > Fluoroquinolones > 3rd/4th generation cephalosporins > Piperacillin-Tazobactam In terms of efficacy against infections with a high microbial load (inoculum effect) [ 241 , 251 , 262 – 263 ] * New antibiotics ( Dalbavancin/Oritavancin [ 264 – 271 ], Ceftaroline [ 235 – 240 , 272 – 277 ], Ceftobiprole [ 278 – 280 ], Ceftolozane-Tazobactam [ 281 – 285 ], Ceftazidime-Avibactam [ 286 – 292 ], Meropenem-Vaborbactam [ 292 – 298 ], Cefiderocol [ 292 , 299 – 313 ], Imipenem-Relebactam [ 314 – 316 ], Delafloxacin [ 317 – 322 ], Cefepime-Enmetazobactam [ 292 , 323 – 325 ], Plazomicin [ 292 , 326 ], Eravacicline [ 327 – 331 ], Aztreonam-Avibactam [ 332 – 336 ], Rezafungin [ 337 – 338 ], etc.) [ 292 , 298 , 339 – 355 ] > conventional or pre-existing antibiotics In general, for severe infections at high-risk patients Carbapenems > Comparators (Piperacillin-Tazobactam, 3rd/4th-generation cephalosporins) With proven clinical impact [ 217 , 253 , 356 – 357 ] * Open in a new tab Hierarchical positioning was based on available clinical evidence together with in vitro bactericidal activity assessed by time-kill curves and PK/PD data. And iti is always relative and contextual (see text). * Indicates that AI has been used in the analysis of that algorithm. The ranking of antimicrobials should always be interpreted in context and not as indicating universal superiority. The relative effectiveness of an antibiotic can vary substantially depending on the infectious syndrome, control of the source of infection, the size of the bacterial inoculum, the degree of specific bacterial resistance present and, most importantly, penetration into the target compartment. Therefore, any proposal for therapeutic prioritisation should explicitly incorporate the site of infection and the anticipated exposure at that site. In other words, in complex infections, it is not enough to choose a microbiologically active antibiotic: it is essential to select the one that offers the best effective exposure at the site where the infection is occurring. [ 358 – 360 ]. It is therefore reasonable, in the case of severe and difficult-to-treat infections in the most at-risk patients, to consider that new antimicrobials may improve the effectiveness of treatment and that their selection may improve the prognosis of these infections ( Figure 3 ). B7. Design the best possible strategy tailored to each individual case, based on optimising the PK/PD profile The aim is to tailor treatment by individualising antibiotic dosages and regimens to meet the complex and demanding conditions of these patients, their specific infections, and the underlying pathogens. Increasing doses, either per dose or by increasing the number of doses, depending on whether the antibiotic is dose- or time-dependent, prolonged or continuous infusions, initial loading doses, and pharmacological and microbiological monitoring of their effects are well-established optimisation strategies for a wide range of antibiotics in many infection models [ 361 – 369 ] ( Figure 3 ). PK/PD optimisation takes on even greater relevance in subgroups with particularly complex pharmacokinetics, such as critically ill patients with renal hyperfiltration, continuous renal replacement therapy, ECMO, obesity, severe hypoalbuminaemia, extensive burns, or massive expansion of the extracellular space. In these scenarios, standard dosing regimens are particularly vulnerable to under-exposure and, consequently, to treatment failure. Thus, ASPs focused on effectiveness must incorporate specific pathways for advanced pharmacotherapeutic validation, therapeutic monitoring where available, early review of doses (loading and subsequent) and infusion (standard, extended or continuous), and a structural role for the clinical pharmacist in the daily audit of highly complex treatments [ 370 – 372 ] ( Figure 5 ). Figure 5. Open in a new tab The complexity of optimising antibiotic therapy for severe infections PK/PD optimisation is a pharmacological issue and can therefore benefit significantly from the contribution of the pharmacy department. The ability to audit prescriptions from a PK/PD perspective, to provide real-time guidance on achieving pre-established and validated PK/PD targets, to monitor antimicrobial concentrations), to help minimise drug interactions and toxicities, and to make the necessary pharmacological adjustments in patients with comorbidities that alter antibiotic metabolism, and to rescue beta-lactam agents through a beta-lactam allergies delabelling programme offers significant opportunities for optimisation, just as the correlation of PK/PD parameters with the quantitative evolution of the bacterial load may also improve effectiveness. We must therefore recognise that pharmacy is a key strategic partner in the pursuit of effectiveness. B8. Consider the improvements in the PK/PD profile that combinations of antimicrobials can offer Historically, combinations of antimicrobials have proven more effective than monotherapy in treating a range of ‘difficult-to-treat’ infections, includin HIV infection, hepatitis C, tuberculosis, brucellosis, Q fever, endocarditis, malaria, toxoplasmosis, etc. They have also proven effective in the most severe infections caused by multi-resistant Gram-negative bacteria [ 80 , 85 , 200 , 202 , 257 , 345 , 369 , 373 – 394 ], multi-resistant Gram-positive bacteria [ 395 – 408 ] and fungi [ 409 – 412 ]. Combination therapy is a strategy of selective intensification in situations with a high risk of early treatment failure, such as septic shock, suspected MDR pathogens with high attributable mortality, persistent bacteraemia, incomplete or delayed control of the source of infection, infection with a high bacterial load, certain forms of profound immunosuppression, the presence of toxins and other virulence factors, etc. Within this framework, combination therapy can lead to improvements in efficacy ( Figure 3 ) that may be critical in the initial treatment of the most complex or difficult-to-treat infections, even in the case of new antibiotics [ 313 , 385 – 394 ]. This involves not only broader coverage, but also a faster reduction in the bacterial load, enhanced PK/PD, inhibition of toxin and superantigen synthesis, and prevention of resistant mutant subpopulations [ 188 , 288 , 413 – 420 ]. The drug interactions involved in combination therapy and their relationship with the mechanisms of microbial resistance are highly complex, have not been sufficiently explored, and give rise to uncertainties that call for caution [ 421 – 425 ]. Furthermore, they can lead to drug antagonism and increase the potential for toxicity and other undesirable drug interactions. However, there is a wealth of literature showing that combinations can hinder the emergence of resistant bacteria during antibiotic treatment [ 81 , 354 – 355 , 420 , 425 – 429 ]: ‘Perhaps the most important use of combination chemotherapy is in attempts to suppress the emergence of resistance’ [ 354 – 355 ]. However, although we are operating in a context of unacceptably high mortality [ 80 , 215 ] and assume that combination antibiotic therapy can offer clinical and ecological benefits, its uptake in clinical practice has been slow and insufficient, perhaps unduly hampered by the inertia generated by the paradigm linking antibiotic pressure to the development of AMR [ 80 ]. Its adoption could be facilitated by the assumption that every indication for combined antibiotic therapy should be subject to early re-evaluation and an explicit simplification strategy once microbiological and clinical uncertainty has diminished. Furthermore, it is necessary to encourage and maintain close scientific monitoring of the risks and benefits of this strategy. B9. Consider other pathogenic factors that influence the effectiveness of antimicrobial treatment Other pathogenic factors, both microbial and host-related, in addition to AMR, can increase the risk of treatment failure, such as virulence, persistence, including in biofilms or ‘biological sanctuaries’, or immune dysfunction generated by the microbial agent and/or the host’s immunoregulatory genotype/phenotype, which may manifest as hyperinflammation, immunoparalysis, etc. This is a real problem and, although it is an underexplored or insufficiently explored area [ 430 – 432 ], its influence on therapeutic effectiveness would compel us to give it greater consideration and to delve deeper into the selection of patients who might benefit from treatments better adapted to these types of factors. On the one hand, we must pay greater attention to potential anti-inflammatory and immunomodulatory adjuvant therapies, which combat hyperinflammation or immunoparalysis, or interfere with pathogenic processes, such as bismuth, gamma interferon, checkpoint inhibitors such as anti-PD1, etc. On the other hand, we must deepen our understanding of, and put into practice, how various antimicrobials address these virulence factors, such as the release of polysaccharides and other endotoxins; or better penetrate biofilms or sanctuaries and maintain their activity against persistent bacteria, or how they better adapt to the patient’s inflammatory or immunological profile [ 430 – 432 ]. B10. Minimise the ecological impact as much as possible in every prescription The emergence of AMR during exposure to antibiotics is their main adverse effect; it is cumulative in nature (it progresses easily and is very difficult to reverse), impacts prognosis in direct proportion to its presence, and is also very difficult to control. It is a highly complex and poorly understood issue, and we have few tools to study it, few targeted studies, little regulatory scrutiny in this regard, and insufficient institutional surveillance. Multiple variables interact in the generation and development of AMR even when their use is appropriate: microbial factors (both individual and ecological), host factors (immune system, microbiome, genotype, habits, comorbidities, etc), and antimicrobial therapy factors (drugs, PK/PD strategies, duration) [ 59 , 343 , 354 – 355 , 425 , 428 – 429 , 433 – 447 ]. It is important to note that the use of antibiotics merely selects for, rather than generates, resistance genes or clones that must already be present, within an altered ecological context that allows for clonal amplification, and under prolonged selective pressure. Moreover, any oversimplification of the problem (for example, ‘more antibiotics = more resistance’ or ‘broad-spectrum antibiotics generate more resistance’) is inaccurate and prevents valid predictions. Although the ecological impact does not determine initial clinical effectiveness, as it does affect future effectiveness but not current effectiveness, and managing it is complex and difficult, its significance is such that every prescription of antibiotics must include an assessment of its ecological impact and of the ability to prevent the emergence of resistance during treatment [ 354 – 355 ]. Such an assessment should also be given greater weight in scientific research, in the marketing of antibiotics and within the health administration, through ecological impact monitoring systems [ 354 – 355 ]. Therefore, in a scenario such as that outlined in this document, which focuses on the pursuit of effectiveness through the intensification of antibiotic pressure, it is necessary to address the challenge of whether maximising therapeutic efficacy could fuel or drive AMR. Minimising the ecological impact is a more demanding objective than achieving a cure. Clinical cure is achieved with a certain level of antibiotic pressure (exceeding PK/PD targets centred on the MIC, which, as it is prolonged, selects for and allows the development of more resistant subpopulations (which would require greater antibiotic pressure, with PK/PD targets centred on the Mutation Preventive Concentration (MPC) for their eradication). However, these two objectives need not be mutually exclusive. The strategy known as ‘front-loading’, which proposes the initial use of the most active and eradicative treatments (with better PK-PD outcomes, which are generally provided by last-line antibiotics, combinations and new strategies – including therapeutic alternatives with adjuvants, inhibitors, potentiators, new formulations, immunomodulators, phages, gene therapies, biomolecules, new products proposed by AI…), administered over very short periods (for this purpose, ideally less than 5 days, probably sufficient up to 7 days), could be a good strategy for reducing the ecological impact, as it would reduce the initial microbial inoculum more rapidly and shorten the duration of selective pressure, whilst also enabling the elimination of more bacterial subpopulations with a higher AMR burden. Although this strategy lacks sufficient clinical evidence, it is conceptually appealing and biologically plausible, and is supported by considerable experimental evidence [ 428 – 429 , 441 – 442 , 444 – 445 , 448 – 455 ] ( Figure 6 ), which, as long as it does not contradict existing clinical evidence, could help us to make this concept more operational and incorporate it into our recommendations. Figure 6. Open in a new tab First smart: the “front loading” strategy can improve effectiveness and ecological impact. Partial data obtained from Paterson IK et al. [ 445 ] We therefore propose that the restrictive nature of ecological control over prescribing practices should be adjusted, allowing for increased antibiotic pressure during the initial phase of treatment in this patient group. This would reduce the likelihood of prescribing suboptimal treatments which, paradoxically, could lead to greater pressure for the selection and spread of AMR and other ecological costs, since treatment failures lead to greater exposure to antibiotics and a greater progressive deterioration of the microbiota. And since the selective pressure of antibiotic therapy, regardless of its complexity and difficult predictability, increases with the duration of exposure, we have better opportunities for ecological control than those affecting the choice of empirical treatment, such as the reductions in exposure provided by early adjustment or de-escalation, and the reduction in treatment duration [ 194 , 445 , 447 , 456 – 475 ]. This type of high-intensity, short-course antibiotic regimen—which, as we have already noted, may have a favourable ecological impact—should be subject to a formal review after 24 hours and a second, decisive review after 48–72 hours, taking into account haemodynamic trends, control of the infection focus, microbiological results, biomarkers, toxicity and PK/PD plausibility. This would serve to adjust or de-escalate treatment, and also to detect treatment failure early and activate rescue strategies without delay (which are all the more effective and ecological the earlier they are implemented). Thus, adjustment or de-escalation and rescue represent two complementary expressions of the same principle: rapidly adjusting antimicrobial pressure to the evolving biological reality of the case. Complementarily, the judicious and individualised adjustment of the duration of antibiotic treatment for each patient and episode is another decisive factor in reducing its ecological impact [ 194 , 445 , 447 , 456 – 475 ]. B11. Consider rescue therapy at an earlier stage It is important to monitor the efficacy of antibiotic treatment in this type of infection and patient and to define treatment failure (and the need for rescue therapy) within the first 48–72 hours of treatment [ 476 ]. In this patient group, the best definition of treatment failure is the absence of significant signs of improvement within the first 48–72 hours of treatment [ 194 , 445 , 449 , 465 – 467 , 476 – 479 ]. In patients who fail initial treatment and have multi-resistant Gram-negative bacteria (GNB), a delay (> 4–5 days) in prescribing rescue therapy with the best available antibiotic options is associated with significantly lower survival, 3–5 times lower [ 216 , 285 , 290 , 302 , 305 , 308 – 312 , 317 ]. It is therefore important to rapidly incorporate the best antibiotics and therapeutic strategies into the guidelines and protocols for empirical rescue treatment for at-risk patients with severe infections. B12. Assessing the importance of convenience in effectiveness Investing in therapeutic convenience can improve the quality of our interventions and their outcomes. Therapeutic convenience should not be regarded as a secondary or merely logistical attribute, but as a clinical determinant of effectiveness, particularly during phases of care transition. The availability of simplified administration formulations, agents with a prolonged half-life or oral alternatives with high bioavailability can reduce delays, administration errors, loss of adherence, avoidable hospital stays and complications related to vascular access. In selected patients, these strategies allow for the maintenance of adequate therapeutic intensity beyond the initial critical period and facilitate models of shortened hospitalisation, outpatient continuity, and outpatient parenteral antimicrobial therapy (OPAT) with greater safety and less organisational friction [ 266 – 267 , 480 – 484 ]. These new formulations reflect scientific and technical progress in pharmacological optimisation (representing the pinnacle of PK-PD optimisation); therefore, in addition to advantages in terms of safety, convenience, adherence, cost-effectiveness and quality of life, they have demonstrated benefits in clinical effectiveness [ 266 – 267 , 485 – 489 ]. B13. Give greater and more effective consideration to the benefits of innovation In the face of the major problem posed by the rise in AMR and the trend in infection-related mortality, the WHO’s programme priorities—based on awareness-raising, surveillance, prevention and optimisation (of existing resources)—are not delivering the expected results [ 23 – 29 ]. There is a need to complement them with a more decisive push towards the incorporation of innovation that is adaptable, sustainable, anticipatory, parameterised, scalable and operational or usable, the benefits of which can be rapidly transferred to patients [ 490 – 494 ] ( Figure 7 ). Figure 7. Open in a new tab The critical importance of innovation. Obtained from OCDE, 2022 [ 492 ] However, accessing innovation is a highly complex and demanding process, with numerous critical points that must be managed effectively to ensure success ( Figure 8 ). Figure 8. Open in a new tab Innovation and effectiveness In the generation of innovation : Political awareness of the need for innovation , on which research and development, budgets depend. Management and direction of scientific research , which, in the field of infections, has not evolved in line with needs. There has likely been a lack of cooperation between the various collaborating stakeholders (academics, government, healthcare professionals, pharmaceutical industry), and the ability to establish appropriate partnerships would be a significant advantage in addressing this issue. In our field, it is often considered that ‘partnering’ with the pharmaceutical industry may undermine the quality of research due to the bias caused by its commercial interests. However, in the real world, it has become evident that economic interests, within a competitive environment and an appropriate regulatory framework, drive progress more— and probably better—than disinterested public initiatives operating ‘outside the competition’. Is it better to use public funds for independent research into new applications for older, commercially ‘orphaned’ antibiotics [ 495 ], or to collaborate with the pharmaceutical industry on research aimed at optimising the use of new antibiotics, thereby prolonging their effectiveness and clinical utility [ 444 , 446 , 452 , 496 ]? And which of these two types of research will benefit the patients on whom this document focuses sooner and more effectively? A methodological review of research processes is also required, which could, for example, broaden, deepen and accelerate the research we need to improve the effectiveness of antimicrobial treatments, as is being done with the integration of AI into such methodology (for example, by investigating, through ‘de-extinction’, antibacterial molecules derived from the microbiomes of extinct animals, or by mining microbial DNA, etc., which is yielding promising and transferable results in far less time than in traditional research) [ 67 , 497 – 502 ]. Funding: Research is very costly. The prevalence of restrictive policies jeopardises the necessary financial return required [ 58 – 59 ], hinders new investment, and deters and drives away research staff [ 503 ], whilst also stifling the necessary diversification and forward-looking nature of research. At present, it seems necessary to review the financial model underpinning the acquisition of innovation, which should probably be refocused more towards ‘payment based on the product’s value to the healthcare system’ or ‘outcome-based’ rather than solely on sales volume [ 504 – 508 ]. As in the previous point, effective coordination between the various stakeholders involved would provide competitive advantages. A well-understood, managed and governed public-private partnership, as well as one that is transparent and meritocratic, continues to appear a reasonable opportunity to improve access to innovation. Translating research into operational innovation: The process of moving from research to the provision of healthcare products capable of addressing unmet needs in a timely and appropriate manner is highly complex and beyond the scope of this document, yet it is fundamental. In the following section, we discuss how this issue affects professionals and what role they could play in accessing innovation in the best possible way. In the transfer of innovation to patients: Scientific regulation : The methodology required to generate scientific evidence is highly complex, to the extent that it jeopardises access to the innovation we need. To make scientific information more actionable, there is a need for more efficient management of the scientific literature and a streamlining of regulatory requirements [ 509 ]. Administrative regulation: The advantage of restricting the use of innovative products to those backed by ‘high-grade’ scientific evidence is that, as well as promoting the appropriate use of these products, it reduces costs. Hence the enduring appeal of restrictive policies for administration, which constitutes another barrier to innovation that needs to be reviewed. Advocacy (by healthcare professionals and patients): Although the prospect of transforming these processes and procedures from the perspective of frontline healthcare professionals seems, given their complexity and scale, highly challenging, the fact remains that these professionals have access—sometimes significant access—to each and every one of the agents and stages involved in generating and regulating innovation, and play a fundamental role in advocating for and transferring this innovation to patients. And these possibilities offer critical opportunities, and therefore clear responsibilities. Opportunities in the design of research and in the interpretation and translation of its results into real-world practice. Also, in advocating for and demanding a commitment from the authorities to jointly review the regulatory process and the financial model underpinning the adoption of innovation. And, finally, within the professional context itself, in healthcare institutions, to streamline and ensure that the transfer of the benefits of innovation to patients takes place in a timely and appropriate manner. Always in line with this drive for effectiveness that we are calling for here, (using all available scientific information, facilitating responsible prescribing without external obstacles, producing guidelines and protocols that are constantly updated and incorporating new developments in real time, etc.). Recognising the value of new information technologies : Currently, within our national framework, a range of digital solutions are already available that enable the optimisation of the selection and use of antimicrobials, support the more efficient management of conventional ASPs, and provide electronic systems to assist with infection and AMR surveillance and control programmes. These include CDSS tools—whether rule-based, predictive model-based or advanced AI-based—ranging from modernised software and apps (WASPSS ® , the “MENSA” Guide to Antimicrobial Therapy ® , the SNS ® Antimicrobial Therapy Guide by AEMPS-PRAN· PROA, Community Infections – Guide to Antibiotic Treatment ® from 9 national scientific societies, PROApp by AEMPS-PRAN Ministry of Health) to applications and products generated using artificial intelligence (AI) already available in the healthcare market (iAST ® by Pragmatech, InNoCBRsystem3 ® , Farmatox ® by In2AI, TheraDoc/Premier Clinical Surveillance ® , WHONET ® , NHSN-AU ® from the CDC, Epic’s EHR ‘Bugsy’ modules, ARGOS ® as a hub for infection surveillance, prevention and control from Quiron Salud and, finally, the highly comprehensive and sophisticated multi-tasking LUMED ® in its APSS ™ and ZINC ™ modules from bioMérieux) ( Table 3 ). Table 3. Comparative review of decision support systems for the stewardship and monitoring of health care-associated infections Product Main purpose EHR/HIS Integration Real-time alerts / AMS Customisation Evidence of impact LUMED ® (APSS/ZIN) (bioMerieux) CDSS for AMS + HAI Surveillance High integration with EHR + LIS Prioritised alerts for ASP teams. APSS module: antimicrobial use optimisation. ZINC module: infection prevention and control. High local adaptability (integration into local guidelines) Documented in institutions (supported by bioMerieux ® ) TheraDoc / Premier clinical surveillance Automated infection surveillance + pharmacy/AMS Very good EHR integration Real-time alerts for pharmacy and IPC (Infection preventionists) High customisation Studies showing cost savings and increased pharmaceutical intervention iAST ® (Pragmatech AI solutions) Private company software using AI predictive algorithms to improve antimicrobial prescribing (web-based tool) Good EHR + LIS + hospital formulary integration (robust calculation engine) Prioritised alerts for ASP teams (based on clinical variables and hospital context) Moderate local adaptability (predicts probability of success for each antibiotic) Early documentation in institutions (supported by Pragmatech); impact on empirical treatment WHONET Free Microbiology / AMR software Imports laboratory data No automated clinical alerts Medium (configurable for laboratory use) Global reference for AMR and resistance data (regional/global) NHSN (CDC) National platform for HAI reporting and benchmarking Integration of aggregated data Not an operational CDSS Low (national standard) National surveillance standard in the USA Open in a new tab CDSS: Clinical Decision Support Systems; AMS: Antimicrobial Stewardship; AMR: antimicrobial resistance; HAI: healthcare-associated infections; EHR: electronic health records; LIS: Laboratory Information Systems; ASP: Antimicrobial Prescribing and Optimisation Support; IPC: infection control practices. C. Proposals for implementing this programme and monitoring its impact on clinical outcomes C14. Dissemination of the programme to make it operational The need to make these proposals operational and influential requires a highly active and professional dissemination policy, which could be implemented through initiatives such as the following: Experts must be involved in horizontal, tree-like and chain-branching training during clinical practice. This could take the form of face-to-face sessions, or as consultations or audits. Consultations or inter-consultations should be considered as opportunities for consultancy. ASPs should promote, based on the awareness and concept of the ASP-COMPLEX: Specific audits to detect and assess the current situation and existing problems Specific informative and audit clinical sessions , scheduled across all relevant areas Collaboration programmes with regular face-to-face activities involving the departments most affected by these issues (including Emergency Department and Primary Care), to share information on the problems, their assessment, specific proposals for improvement and progress. Specific training courses on these aspects. Work with the administration to integrate the ASP-Complex objectives into service management agreements, including programme contracts, and into the assessment of compliance with agreed objectives (to help improve them), identifying key objectives that facilitate productive interaction. Incorporation of the key concepts of this programme into local clinical guidelines, microbiological maps, electronic prescription systems, the sepsis code programme and other protocols in any format. Incorporation of new computerised programmes , information and communication technologies (ICT) or AI-guided systems, which can make the acquisition and transmission of key knowledge more efficient. C15. Systematic surveillance of improvement initiatives In order to achieve and consolidate improvements, it is necessary to incorporate systematic surveillance of optimisation initiatives . Monitoring is carried out on the basis of indicators, of which there are three types: 1) Structural , including the existence of the ASP and experts, antibiotic policies, AMR monitoring programmes, guidelines, educational programmes, etc.; 2) Process-related , including recording of antibiotic prescriptions, 48–72-hour review, adherence to ASP recommendations, guidelines or pre-authorisation processes, frequency of de-escalation, oral-to-IV sequencing, time to initiation of antibiotic therapy, culture sampling prior to treatment initiation, etc.; and 3) Outcome-related , including monitoring of antibiotic consumption and expenditure, adverse effects, and, most importantly, clinical outcomes: mortality, cure rates, hospital stays, readmissions) [ 51 , 118 – 119 , 510 – 511 ]. Since these are initiatives to improve clinical outcomes, the first requirement is the mortality registry, broken down by Health Areas and Infectious Syndromes, and the analysis of causes of mortality, through ASPs, Mortality committees, etc., and their relationship with antibiotic treatment, including timing, choice of empirical treatment, choice of targeted treatment, whether its efficacy has been assessed in a timely and appropriate manner and whether rescue treatment has been considered, patient vulnerability and the type of infection. Furthermore, for every optimisation initiative that is successfully implemented, a specific indicator must be added—preferably one focused on clinical outcomes—that correlates with evidence demonstrating benefits in both individual and overall clinical health outcomes, and that enables monitoring, analysis and comparison, both over time within the centre itself, and with other healthcare centres through benchmarking. And this must be done despite the fact that the set of validated indicators for measuring effectiveness is limited [ 4 , 51 , 94 , 117 – 119 ]. The following could serve as potential quality indicators for each of the proposals we have submitted: A1. Achieving earlier and more widespread diagnosis of severe infection To measure the delay in the diagnosis of severe infection : ‘Door-to-needle time’ or ‘time to antibiotics’ [ 121 , 123 , 178 – 187 , 512 – 517 ] – which would measure the time elapsed from the patient’s arrival at the Emergency Department until the administration of the first dose of antibiotics. If new diagnostic technologies are available, the ‘door-to-diagnosis time’, defined as the time between admission to the Emergency Department and the triggering of an alarm for sepsis or severe/complex infection. The incorporation of vulnerability assessment into any protocol for managing severe infection. Implementation of an automated system for diagnosis and severity categorisation using AI in place. A2. Identifying and controlling the source of infection more rapidly Control of the focus within < 6 hours (when instrumental or surgical intervention is required) [ 121 , 123 , 518 – 519 ]. Verify in clinical records that a systematic and structured search for the focus has been carried out from the moment of diagnosis of the infection. A3. Achieving an earlier, more comprehensive and accurate microbiological diagnosis Availability of continuous microbiological support 24/7”). Adoption, adaptation and implementation of PRODIM [ 520 – 521 ]. Availability of an optimised, as defined above, and updated, at least annually, local microbiological map. Full and permanent integration of the microbiological map into the local antibiotic therapy guidelines, to ensure that its recommendations remain up to date and validated. To measure diagnostic delay: Time to microbiological diagnosis, from the moment samples are received by the Microbiology Laboratory. Time from admission to communication of microbiological result, from admission to Emergency Department until the publication of the microbiological test results. B4. Immediate initiation of empirical antibiotic therapy ‘Door-to-needle time’ or ‘time to antibiotics’ [ 121 , 123 , 178 – 187 , 512 – 517 ]. B5. Selection of an appropriate, active and effective empirical antibiotic therapy Success rate of empirical antimicrobial treatment. Success implies that the treatment and the chosen dosage regimen are microbiologically active or effective. This applies to serious infections confirmed by microbiological culture, such as bacteraemia, sepsis and septic shock, endocarditis, meningitis, nosocomial infections, etc. [ 80 , 84 – 85 , 140 , 188 – 205 , 522 ]. B6 to B9. From efficacy to effectiveness: how can we achieve effective treatment or the best possible treatment for this type of patient? There are no valid indicators that are easy to apply. The assessment of whether an active treatment was the best possible (in terms of choice of drugs, adaptation of PK/PD optimisation to the patient’s specific conditions, consideration of the use of combinations or other pathogenic factors other than AMR) must be carried out individually and specifically by experts, which requires time that is usually not available. One could begin by assessing cases of clinical failure or mortality in patients receiving antibiotic therapy. And the indicators to be generated could be based on concepts such as: Appropriateness of targeted treatment . Was it the most effective? (Appropriateness implies, in patients with infections carrying a high risk of mortality, optimal or maximum available effectiveness or efficacy) Time to effective or most effective treatment available. B10. Minimise the ecological impact as much as possible in every prescription In addition to the standard indicators of antimicrobial consumption, exposure and expenditure, and the evolution of resistance [ 194 , 447 ]: Emergence of resistance during treatment. Rate of and time to appropriate de-escalation, with and without microbiological information. Rate of and time to appropriate treatment discontinuation. Other possible indicators: Use of antimicrobial plasma level monitoring in selected indicated cases. Rate of adherence to or monitoring of local guidelines, or of prescribing protocols integrated into electronic prescribing, or of AI-based CDSS tools, etc. Time to microbiological eradication, defined as the time until blood cultures become negative, or until there is a significant reduction in bacterial load. B11. Consider rescue therapy at an earlier stage Time to rescue. Defined as the the time from when treatment failure is defined within 48–72 hours until rescue therapy is initiated. B12. Assessing the importance of convenience in effectiveness Rate of missed opportunities to improve effectiveness through the use of modern antimicrobials with optimised PK/PD for improved dosing convenience in specific clinical situations. B13. Give greater and more effective consideration to the benefits of innovation Although we do not have validated indicators to assess our ability to incorporate innovation and transfer it to patients, we should add to the monitoring process certain assessment criteria or indicators that can help to drive this forward. Aspects such as the following could be considered: Time taken to acquire innovative products. Defined as the time from market launch to availability at each centre, analysing the reasons for the delay, which may include, at more demanding levels, delays by the regulatory agency, difficulties in integrating various hospital programmes into a single system or digital solution based on advanced AI, or delays by the administration in marketing it). Analysis of missed opportunities to improve effectiveness due to the unavailability of innovations already on the market, in any scenario (empirical, targeted or rescue treatment, clinical decision-support systems based on digital tools, etc.) and in any difficult-to-treat infection. Initiatives to disseminate the potential benefits of the acquired innovation, including dissemination times, from awareness of the new drug, diagnostic technique or operating system to all potential prescribers, users or consultants. Acquisition-to-prescription time, defined as the time from when a point a product is acquired and made available until its first use or regular use by the areas or departments involved. The time taken for new products and strategies to be incorporated into clinical guidelines, microbiological maps or hospital digital management systems. 4. Conclusions At a time of great concern regarding trends in morbidity and mortality from infections, and mindful of the limitations of our current approaches to combating them, this document sets out a structured programme of proposals aimed at improving, in the short and medium term, the survival of the most at-risk patients with severe infections ( Figure 9 ). Figure 9. Open in a new tab Conceptual and operational summary of the ASP- COMPLEX (a complementary proposal aimed at ASPs and designed to improve the treatment and prognosis of serious infections in high-risk patients). These are complementary proposals which, on the one hand, reinforce already established initiatives and, on the other, introduce some novel aspects to the approach to this complex problem. They focus on accelerating, standardising, and refining the clinical diagnosis of severe infection and its microbiological diagnosis and, above all, on improving the effectiveness of antimicrobial treatment, based on its immediate initiation with the greatest anticipated effectiveness available, and with an improved capacity to integrate innovation into all stages and processes. This programme is designed to be implemented within the framework of the antimicrobial stewardship programmes (ASP) (hence the name ASP-COMPLEX), and requires appropriate dissemination and surveillance policies, alongside a collaborative environment among the multiple stakeholders involved in delivering innovation (experts, stewardship teams, healthcare administration, pharmaceutical industry, etc.). In high-risk patients with severe infections, antimicrobial stewardship should not only aim to reduce unnecessary antibiotic exposure, but also to ensure that every patient receives the earliest and most effective treatment possible. Footnotes AI statement Artificial intelligence tools were used to create Figures 2 , 5 , and 9 . The tools used were Gemini and Note-bookLM. All AI-generated content was reviewed, edited and approved by the authors, who take full responsibility for the accuracy, integrity and final content of the figures and the manuscript. Funding None to declare. Conflicts of interest No commercial, financial, or personal conflicts of interest were identified in relation to the research, authorship, and/or publication of this article. 5. References 1. Rudd K, Johnson S, Agesa K, Shackelford K, Tsoi D, Kievlan Det al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet 2020; 395: 200–211. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Shlaes DM, Gerding DN, John JF Jr, Craig WA, Bornstein DL, Duncan RAet al. Society for Healthcare Epidemiology of America and Infectious Diseases Society of America joint committee on the prevention of antimicrobial resistance: guidelines for the prevention of antimicrobial resistance in hospitals. Clin Infect Dis. 1997;25(3):584–99. Available from: 10.1086/513766 [ DOI ] [ PubMed ] [ Google Scholar ] 3. Podolsky SH, . The evolving response to antibiotic resistance (1945–2018). Palgrave Commun. 2018;4(1). Available from: 10.1057/s41599-018-0181-x [ DOI ] [ Google Scholar ] 4. Davey P, Marwick CA, Scott CL, Charani E, McNeil K, Brown Eet al. Interventions to improve antibiotic prescribing practices for hospital inpatients. Cochrane Database Syst Rev. 2017;2(2):CD003543. Available from: 10.1002/14651858.CD003543.pub4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Talbot GH, Bradley J, Edwards JE Jr, Gilbert D, Scheld M, Bartlett JGet al. Antimicrobial Availability Task Force of the Infectious Diseases Society of America . Bad bugs need drugs: an update on the development pipeline from the Antimicrobial Availability Task Force of the Infectious Diseases Society of America. Clin Infect Dis. 2006;42(5):657–68. Available from: 10.1086/499819. Erratum in: Clin Infect Dis. 2006;42(7):1065. [ DOI ] [ PubMed ] [ Google Scholar ] 6. Boucher HW, Talbot GH, Bradley JS, Edwards JE, Gilbert D, Rice LB, et al. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin Infect Dis. 2009;48(1):1–12. Available from: 10.1086/595011 [ DOI ] [ PubMed ] [ Google Scholar ] 7. Dellit TH, Owens RC, McGowan JE Jr, Gerding DN, Weinstein RA, Burke JP, et al. Infectious Diseases Society of America; Society for Healthcare Epidemiology of America. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis. 2007;44(2):159–77. Available from: 10.1086/510393. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Rodríguez-Baño J, Paño-Pardo JR, Álvarez-Rocha L, Asensio A, Calbo E, Cercenado E, et al. Programas de optimización de uso de antimicrobianos (PROA) en hospitales españoles: documento de consenso GEIH-SEIMC, SEFH y SEMPSPH. Enferm Infecc Microbiol Clin. 2012;30(1):53–61. Available from: 10.1016/j.eimc2011.09.018 [ DOI ] [ PubMed ] [ Google Scholar ] 9. Editorial . The antibiotic alarm. Nature 2013;495(7440):141. Available from: 10.1038/495141a [ DOI ] [ PubMed ] [ Google Scholar ] 10. O’Neill J. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. London: Review on Antimicrobial Resistance; 2014. [ Google Scholar ] 11. World Health Organization . Global action plan on antimicrobial resistance. Geneva: World Health Organization; 2015. Available from: https://apps.who.int/iris/bitstream/handle/10665/193736/9789241509763_eng.pdf [ DOI ] [ PubMed ] [ Google Scholar ] 12. United Nations . Transforming our world: the 2030 Agenda for Sustainable Development. New York: United Nations; 2015. Available from: https://sdgs.un.org/2030agenda [ Google Scholar ] 13. European Centre for Disease Prevention and Control . Surveillance of antimicrobial resistance in Europe: annual report of the European Antimicrobial Resistance Surveillance Network (EARS-Net) 2017. Stockholm: ECDC; 2018. [ Google Scholar ] 14. World Health Organization . Monitoring and evaluation of the global action plan on antimicrobial resistance: framework and recommended indicators. Geneva: World Health Organization; 2019. Available from: https://apps.who.int/iris/bitstream/handle/10665/325006/9789241515665-eng.pdf [ Google Scholar ] 15. World Health Organization . In the face of slow progress, WHO offers a new tool and sets a target to accelerate action against antimicrobial resistance. Geneva: World Health Organization; 2019. Available from: https://www.who.int/news/item/18-06-2019-in-the-face-of-slow-progress-who-offers-a-new-tool-and-sets-a-target-to-accelerate-action-against-antimicrobial-resistance [ Google Scholar ] 16. World Health Organization . WHO AWaRe (Access, Watch, Reserve) antibiotic book. Geneva: World Health Organization; 2022. Available from: https://www.who.int/publications/i/item/WHO-MHP-HPS-EML-2022.02 [ Google Scholar ] 17. Mudenda S, Daka V, Matafwali SK. World Health Organization AWaRe framework for antibiotic stewardship: Where are we now and where do we need to go? An expert viewpoint. Antimicrob Steward Healthc Epidemiol. 2023;3(1):e84. Available from: 10.1017/ash.2023.164 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Zanichelli V, Sharland M, Cappello B, Moja L, Getahun H, Pessoa-Silva C, et al. The WHO AWaRe (Access, Watch, Reserve) antibiotic book and prevention of antimicrobial resistance. Bull World Health Organ. 2023;101(4): 290–6. Available from: https://digitalcommons.wustl.edu/oa_4/1628 [ Google Scholar ] 19. World Health Organization . Global research agenda for antimicrobial resistance in human health. Geneva: World Health Organization; 2023. Available from: https://www.who.int/publications/m/item/global-research-agenda-for-antimicrobial-resistance-in-human-health [ Google Scholar ] 20. Organización Mundial de la Salud . Una agenda mundial de salud para 2025-2028: decimocuarto Programa General de Trabajo. Ginebra: OMS; 2024. Available from: https://iris.who.int/ [ Google Scholar ] 21. Centers for Disease Control and Prevention . Core elements of hospital antibiotic stewardship programs. Atlanta: CDC; 2024. Available from: https://www.cdc.gov/antibiotic-use/hcp/core-elements/hospital.html [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Agencia Española de Medicamentos y Productos Sanitarios , Plan Nacional Resistencia a Antibióticos. Informe anual 2019-2020. Madrid: AEMPS; 2020. Available from: https://www.resistenciaantibioticos.es [ Google Scholar ] 23. European Antimicrobial Resistance Collaborators . The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis. Lancet Public Health. 2022;7(11):e897–913. Available from: 10.1016/S2468-2667(22)00225-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Bauer M, Gerlach H, Vogelmann T, Preissing F, Stiefel J, Adam D. Mortality in sepsis and septic shock in Europe, North America and Australia between 2009 and 2019-results from a systematic review and meta-analysis. Crit Care. 2020;24(1):239. Available from: 10.1186/s13054-020-02950-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Murray CJL, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629–55. Available from: [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Ikuta KS, Swetschinski LR, Robles Aguilar G, Mestrovic T, Gray AP, Davis Weaver N, et al. Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2022;400(10369):2221–48. Available from: 10.1016/S0140-6736(22)02185-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Naghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, Wool EE, et al. Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050. Lancet. 2024;404(10459): 1199–226. Available from: 10.1016/S0140-6736(24)01867-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Peñalva G, Cantón R, Pérez-Rodríguez MT, González-López JJ, Rodríguez-Baño J, del Barrio-Tofiño E, et al. Burden of bacterial antimicrobial resistance among hospitalised patients in Spain: Findings from three nationwide prospective studies. The Lancet Regional Health – Europe. 2025;51:101220. Available from: 10.1016/j.lanepe.2025.101220. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. World Health Organization . WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS): global antibiotic resistance surveillance report, October 2025: summary. Geneva: World Health Organization; 2025. Available from: 10.2471/B09585 [ DOI ] [ Google Scholar ] 30. Friedman ND, Temkin E, Carmeli Y. The negative impact of antibiotic resistance. Clin Microbiol Infect 2016;22(5): 416–422. Available from: 10.1016/j.cmi.2015.12.002 [ DOI ] [ PubMed ] [ Google Scholar ] 31. Morris AM. Antimicrobial Stewardship Programs: Appropriate Measures and Metrics to Study their Impact. Curr Treat Options Infect Dis. 2014;6(2):101–112. Available from: 10.1007/s40506-014-0015-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Agencia Española de Medicamentos y Productos Sanitarios , Plan Nacional Resistencia a Antibióticos. Informe anual 2022-2023 Madrid: AEMPS; 2024. Available from: https://www.resistenciaantibioticos.es [ Google Scholar ] 33. Rodríguez-Baño J. New drugs against multidrug-resistant Gram-negatives: endangered species. Clin Microbiol Infect. 2025. Available from: 10.1016/j.cmi.2025.11.022 [ DOI ] [ PubMed ] [ Google Scholar ] 34. Cogliati Dezza F, Olivares Navarro P, Caponcello MG, Salazar Moscoso M, Cosano Pérez P, Retamar Gentil P, et al. An international Delphi survey on priorities in antimicrobial resistant infections therapeutic research: A preliminary study of MePRAM project. J Infect Public Health. 2026;19(1):103033. Available from: 10.1016/j.jiph.2025.103033 [ DOI ] [ PubMed ] [ Google Scholar ] 35. Gao Y, Guyatt G, Uyeki T, Liu M, Chen Y, Zhao Y, et al. Antivirals for treatment of severe influenza: a systematic review and network meta-analysis of randomised controlled trials. Lancet. 2024;404(10454):753–763. Available from: 10.1016/S0140-6736(24)01307-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Gao Y, Zhao Y, Liu M, Luo S, Chen Y, Chen X, et al. Antiviral Medications for Treatment of Nonsevere Influenza: A Systematic Review and Network Meta-Analysis. JAMA Intern Med. 2025;185(3):293–301. Available from: 10.1001/jamainternmed.2024.7193 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Organisation for Economic Co-operation and Development , European Centre for Disease Prevention and Control. Antimicrobial resistance in the EU/EEA: a One Health response [Internet]. Paris: OECD; 2022. Available from: https://www.ecdc.europa.eu/sites/default/files/documents/antimicrobial-resistance-poli-cy-brief-2022.pdf [ Google Scholar ] 38. Cooke E, Rendi-Wagner P. Rising antimicrobial resistance in Europe: EMA and ECDC outline urgent actions to achieve EU targets. Lancet Reg Health Eur. 2025;59:101539. Available from: 10.1016/j.lanepe.2025.101539 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. European Centre for Disease Prevention and Control . Antimicrobial resistance in the EU/EEA (EARS-Net): annual epidemiological report 2023. Stockholm: ECDC; 2024. [ Google Scholar ] 40. Aracil García B, Oteo Iglesias J, miembros de la red EARS-Net España . Informe de vigilancia: resistencia a los antibióticos en aislados invasivos en España: memoria anual 2024 de la Red Europea EARS-Net. Majadahonda (Madrid): Instituto de Salud Carlos III; 2026. Available from: https://hdl.handle.net/20.500.12105/26128 [ Google Scholar ] 41. Organisation for Economic Co-operation and Development . Embracing a One Health framework to fight antimicrobial resistance. Paris: OECD Publishing; 2023. Available from: 10.1787/ce44c755-en [ DOI ] [ Google Scholar ] 42. Okeke IN, de Kraker MEA, Van Boeckel TP, Kumar CK, Schmitt H, Gales AC, et al. The scope of the antimicrobial resistance challenge. Lancet. 2024;403(10442):2426–38. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Lewnard J, Charani E, Gleason A, Hsu LY, Khan WA, Karkey A, et al. Burden of bacterial antimicrobial resistance in low-income and middle-income countries avertible by existing interventions: an evidence review and modelling analysis. Lancet 2024;403:2439–54. Available from: 10.1016/S0140-6736(24)00862-6 [ DOI ] [ PubMed ] [ Google Scholar ] 44. Mendelson M, Lewnard J, Sharland M, Cook A, Pouwels K, Alimi Y, et al. Ensuring progress on sustainable access to effective antibiotics at the 2024 UN General Assembly: a target-based approach. Lancet. 2024;403: 2551–2564. Available from: 10.1016/S0140-6736(24)01019-5 [ DOI ] [ PubMed ] [ Google Scholar ] 45. Cassini A, Högberg L, Plachouras D, Quatrocchi A, Hoxha A, Simonsen G, et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis. 2019;19(1):56–66. Available from: 10.1016/S1473-3099(18)30605-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. United Nations General Assembly . Political declaration of the high-level meeting on antimicrobial resistance. New York: United Nations; 2024. Available from: https://www.un.org/pga/wp-content/uploads/sites/108/2024/05/20240520-AMR-Political-Declaration-Zero-Draft_FINAL.pdf [ Google Scholar ] 47. Moore CE, Joshi LT. Knocking Out Antimicrobial Resistance editorial: reflections on the United Nations General Assembly high-level meeting on antimicrobial resistance. J Med Microbiol. 2024;73(11):001937. Available from: 10.1099/jmm.0.001937. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Cisneros-Herreros JM, Peñalva-Moreno G. La crisis de los antibióticos: profesionales sanitarios, ciudadanos y políticos, todos somos responsables. Enf Inf Microbiol Clin. 2018; 36(5):259–261. Available from: 10.1016/j.eimc.2018.02.002 [ DOI ] [ PubMed ] [ Google Scholar ] 49. Cercenado E, Rodríguez-Baño J, Alfonso JL, Martínez JA, Sandiumenge A, Llinares P, et al. Antimicrobial stewardship in hospitals: expert recommendation guidance document for activities in specific populations, syndromes and other aspects (PROA-2) from SEIMC, SEFH, SEMPSPGS, SEMICYUC and SEIP. Enferm Infecc Microbiol Clin (Engl Ed). 2023;41(4):238–42. Available from: 10.1016/j.eimce.2022.05.013 [ DOI ] [ PubMed ] [ Google Scholar ] 50. European Centre for Disease Prevention and Control . Last-line antibiotics are failing: options to address this urgent threat to patients and healthcare systems [Internet]. Stockholm: ECDC; 2016. Available from: https://www.ecdc.europa.eu/en/publications-data/last-line-anti-biotics-are-failing-options-address-urgent-threat-patients-and [ Google Scholar ] 51. Schuts EC, Hulscher MEJL, Mouton JW, Verduin CM, Stuart JWTC, Overdiek HWPM, et al. Current evidence on hospital antimicrobial stewardship objectives: a systematic review and meta-analysis. Lancet Infect Dis. 2016;16(7): 847–56. Available from: 10.1016/S1473-3099(16)00065-7 [ DOI ] [ PubMed ] [ Google Scholar ] 52. Bell BG, Schellevis F, Stobberingh E, Goossens H, Pringle M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect Dis. 2014;14:13. Available from: 10.1186/1471-2334-14-13 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Cantón R, Horcajada JP, Oliver A, Garbajosa PR, Vila J. Inappropriate use of antibiotics in hospitals: the complex relationship between antibiotic use and antimicrobial resistance. Enferm Infecc Microbiol Clin. 2013;31 Suppl 4:3–11. Available from: 10.1016/S0213-005X(13)70126-5 [ DOI ] [ PubMed ] [ Google Scholar ] 54. Abejew AA, Wubetu GY, Fenta TG. Relationship between antibiotic consumption and resistance: a systematic review. Can J Infect Dis Med Microbiol. 2024;2024:9958678. Available from: 10.1155/2024/9958678 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 55. Pham Y, Wozniak TM. Systems thinking to understand the complexity of antimicrobial resistance across One Health: a systematic review of current approaches. One Health. 2025;20:101081. Available from: 10.1016/j.onehlt.2025.101081 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 56. DePestel DD, Eiland EH, Lusardi K, Destache CJ, Mercier RC, McDaneld PM, et al. Assessing appropriateness of antimicrobial therapy: in the eye of the interpreter. Clin Infect Dis. 2014;59 Suppl 3:S154–61. Available from: 10.1093/cid/ciu548 [ DOI ] [ PubMed ] [ Google Scholar ] 57. Spivak ES, Cosgrove SE, Srinivasan A. Measuring Appropriate Antimicrobial Use: Attempts at Opening the Black Box. Clin Infect Dis. 2016; 63(12):1639–1644. Available from: 10.1093/cid/ciw658 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 58. Spellberg B, Guidos R, Gilbert D, Bradley J, Boucher HW, Scheld WM, et al. ; Infectious Diseases Society of America . The epidemic of antibiotic-resistant infections: a call to action for the medical community from the Infectious Diseases Society of America. Clin Infect Dis. 2008;46(2):155–64. Available from: 10.1086/524891. [ DOI ] [ PubMed ] [ Google Scholar ] 59. Spellberg B, Bartlett JG, Gilbert DN. The future of antibiotics and resistance. N Engl J Med. 2013;368(4):299–302. Available from: 10.1056/NEJMp1215093. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. Wedell N, Hosken DJ. Three billion years of research and development. Nat Ecol Evol. 2017;1:35. Available from: 10.1038/s41559-016-0035 [ DOI ] [ PubMed ] [ Google Scholar ] 61. Larsen J, Raisen CL, Ba X, Sadgrove NJ, Padilla-González GF, Simmonds MSJ, et al. Emergence of methicillin resistance predates the clinical use of antibiotics. Nature. 2022;602(7895):135–41. Available from: 10.1038/s41586-021-04265-w [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Bhullar K, Waglechner N, Pawlowski A, et al. Antibiotic resistance is prevalent in an isolated cave microbiome. PLoS One. 2012;7(4):e34953. Available from: 10.1371/journal.pone.0034953 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 63. Kashuba E, Dmitriev AA, Kamal SM, Melefors O, Griva G, Römling U, et al. Ancient permafrost staphylococci carry antibiotic resistance genes. Microb Ecol Health Dis. 2017;28(1):1345574. Available from: 10.1080/16512235.2017.1345574 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 64. Agudo R, Reche MP. Revealing antibiotic resistance’s ancient roots: insights from pristine ecosystems. Front Microbiol. 2024;15:1445155. Available from: 10.3389/fmicb.2024.1445155 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Paun VI, Itcus C, Lavin P, Chifiriuc MC, Purcarea C. First genome sequenceand functional profiling of Psychrobacter SC65A.3 preserved in 5,000-year-old cave ice: insights into ancient resistome, antimicrobial potential, and enzymatic activities. Front Microbiol. 2026;16:1713017. Available from: 10.3389/fmicb.2025.1713017 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Clemente JC, Pehrsson EC, Blaser MJ, Sandhu K, Gao Z, Wang B, et al. The microbiome of uncontacted Amerindians. Sci Adv. 2015;1(3):e1500183. Available from: 10.1126/sciadv.1500183 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Wan F, Torres MDT, Peng J, de la Fuente-Núñez C. Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nat Biomed Eng. 2024;8(7): 854–71. Available from: 10.1038/s41551-024-01201-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. von Wintersdorff CJH, Penders J, van Niekerk JM, Mills ND, Majumder S, van Alphen LB, et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front Microbiol. 2016;7:173. Available from: 10.3389/fmicb.2016.00173 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. de Sousa JM, Lourenço M, Gordo I. Horizontal gene transfer among host-associated microbes. Cell Host Microbe. 2023;31(4):513–27. Available from: 10.1016/j.chom.2023.03.017 [ DOI ] [ PubMed ] [ Google Scholar ] 70. Borodovich T, Shkoporov AN, Ross RP, Hill C. Phage-mediated horizontal gene transfer and its implications for the human gut microbiome. Gastroenterol Rep (Oxf). 2022;10:goac012. Available from: 10.1093/gastro/goac012 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 71. Zhang Y, Guo Y, Qiu T, Gao M, Wang X. Bacteriophages: underestimated vehicles of antibiotic resistance genes in the soil. Front Microbiol. 2022;13:936267. Available from: 10.3389/fmicb.2022.936267 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 72. Baquero F. From pieces to patterns: evolutionary engineering in bacterial pathogens. Nat Rev Microbiol. 2004;2(6):510–8. [ DOI ] [ PubMed ] [ Google Scholar ] 73. Brown-Jaque M, Calero-Cáceres W, Espinal P, Rodríguez-Navarro J, Miró E, González-López JJ, et al. Antibiotic resistance genes in phage particles isolated from human faeces and induced from clinical bacterial isolates. Int J Antimicrob Agents. 2018;51(3):434–42. Available from: 10.1016/j.ijantimicag.2017.11.014 [ DOI ] [ PubMed ] [ Google Scholar ] 74. Lederberg J. Infectious history. Science. 2000;288(5464):287–93. Available from: 10.1126/science.288.5464.287 [ DOI ] [ PubMed ] [ Google Scholar ] 75. Densen P. Challenges and opportunities facing medical education . Trans Am Clin Climatol Assoc. 2011;122:48–58. [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. Yusuf E, Ong DS, Martin-Quiros A, Skevaki C, Cortez J, Dedić K, et al. A large survey among European trainees in clinical microbiology and infectious disease on training systems and training adequacy: identifying the gaps and suggesting improvements. Eur J Clin Microbiol Infect Dis. 2017;36(2):233–42. Available from: 10.1007/s10096-016-2791-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 77. GOV.UK . Antimicrobial prescribing and stewardship competency framework [Internet]. London: GOV.UK; 2025. [cited 2026 Apr 14]. Available from: https://www.gov.uk/government/publications/antimicrobial-prescribing-and-stewardship-competencies [ Google Scholar ] 78. Open Evidence ® [Internet]. Available from: https://www.open-evidence.com 79. Retamar-Gentil P, Alegre-Albendea M, Valiente de Santis L, Castón-Osorio JJ, Márquez-Gómez I, Corzo-Delgado JE, et al. How to use novel antimicrobials beyond official indications: an expert consensus. JAC Antimicrob Resist. 2025;7(6):dlaf184. Available from: 10.1093/jacamr/dlaf184 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 80. Gutiérrez-Gutiérrez B, Salamanca E, de Cueto M, Hsueh PR, Viale P, Paño-Pardo JR, et al. Effect of appropriate combination therapy on mortality of patients with bloodstream infections due to carbapenemase-producing Enterobacteriaceae (INCREMENT): a retrospective cohort study. Lancet Infect Dis. 2017;17(7):726–34. Available from: 10.1016/S1473-3099(17)30228-1 [ DOI ] [ PubMed ] [ Google Scholar ] 81. Boyd SE, Moore LSP, Rawson TM, Hope WW, Holmes AH. Combination therapy for carbapenemase-producing Enterobacteriaceae: INCREMENT-al effect on resistance remains unclear. Lancet Infect Dis. 2017;17(9):899– 900. Available from: 10.1016/S1473-3099(17)30450-4 [ DOI ] [ PubMed ] [ Google Scholar ] 82. Gutiérrez-Gutiérrez B, Bonomo RA, Carmeli Y, Paterson DL, Pascual A, Rodríguez-Baño J. Combination therapy for bloodstream infections with carbapenemase-producing Enterobacteriaceae: authors’ reply. Lancet Infect Dis. 2017;17(10):1020–1. Available from: 10.1016/S1473-3099(17)30522-4 [ DOI ] [ PubMed ] [ Google Scholar ] 83. Paul M, Lador A, Grozinsky-Glasberg S, Leibovici L. Beta lactam antibiotic monotherapy versus beta lactam-aminoglycoside antibiotic combination therapy for sepsis. Cochrane Database Syst Rev. 2014;2014(1):CD003344. Available from: 10.1002/14651858.CD003344.pub3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 84. Martinez-Nadal G, Puerta-Alcalde P, Gudiol C, Cardozo C, Albasanz-Puig A, Marco F, et al. Inappropriate empirical antibiotic treatment in high-risk neutropenic patients with bacteremia in the era of multidrug resistance. Clin Infect Dis. 2020;70(6):1068–74. Available from: 10.1093/cid/ciz319 [ DOI ] [ PubMed ] [ Google Scholar ] 85. Chumbita M, Puerta-Alcalde P, Yáñez L, Cuesta MA, Chinea A, Español Morales I, et al. Resistance to empirical β-lactams recommended in febrile neutropenia guidelines in Gram-negative bacilli bloodstream infections in Spain: a multicentre study. J Antimicrob Chemother. 2022;77(7):2017–23. Available from: 10.1093/jac/dkac135 [ DOI ] [ PubMed ] [ Google Scholar ] 86. Price D. Impact of antibiotic restrictions: the physician’s perspective. Clin Microbiol Infect. 2006;12 Suppl 5:3–9. Available from: 10.1111/j.1469-0691.2006.01525.x [ DOI ] [ PubMed ] [ Google Scholar ] 87. Garau J. Impact of antibiotic restrictions: the ethical perspective. Clin Microbiol Infect. 2006;12 Suppl 5:16–24. Available from: 10.1111/j.1469-0691.2006.01527.x [ DOI ] [ PubMed ] [ Google Scholar ] 88. Farmaindustria . El acceso a los medicamentos en España: diagnóstico y recomendaciones [Internet]. Madrid: Farmaindustria; 2022. Available from: https://www.sanidad.gob.es/profesionales/farmacia/pdf/20220615_Recoms_Finales_LE2_1LE2_2_CAPF_v15.pdf [ Google Scholar ] 89. Past E, Hartmann L, Zimmermann R, Zimmermann G, Wallner M, Walter L, et al. Targeting antimicrobial restriction: outcomes of pharmacist-led stewardship interventions at the University Hospital Salzburg. Infection. 2025;53(6):2619–32. Available from: 10.1007/s15010-025-02604-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 90. Bassetti M, Cardone A, Cardoso F, Carter V, Cornely OA, Falcone M, et al. Can we lower the burden of antimicrobial resistance (AMR) in heavily immunocompromised patients? A narrative review and call to action. Infect Dis Ther. 2025;14(9):2029–52. Available from: 10.1007/s40121-025-01204-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 91. Krishnamoorthy Y, Gavindan D, Karunakaran M, Parveen M, Manohar A, Al-Tawfiq JA. Global impact of antimicrobial stewardship programs in healthcare: an umbrella review of effectiveness, cost-efficiency, and implementation strategies. J Infect Chemother. 2025;31(8):102753. Available from: 10.1016/j.jiac.2025.102753 [ DOI ] [ PubMed ] [ Google Scholar ] 92. Leibovici L, Paul M, Ezra O. Ethical dilemmas in antibiotic treatment. J Antimicrob Chemother. 2012;67(1):12–6. Available from: 10.1093/jac/dkr425. [ DOI ] [ PubMed ] [ Google Scholar ] 93. Racionalidad Pinker S.. Barcelona: Paidós; 2021. p. 13. [ Google Scholar ] 94. Dodds Ashley ES, Kaye KS, DePestel DD, Hermsen ED. Antimicrobial stewardship: philosophy versus practice. Clin Infect Dis. 2014;59 Suppl 3:S112–21. Available from: 10.1093/cid/ciu546 [ DOI ] [ PubMed ] [ Google Scholar ] 95. Lakatos B, Farkas FB, Guido G, Saracino A, Di Gennaro F. Addressing infectious diseases in vulnerable populations under the auspices of One Health: a call for action in Europe. Infect Dis Rep. 2026;18(1):12. Available from: 10.3390/idr18010012 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 96. Bladon S, Ashiru-Oredope D, Cunningham N, Pate A, Martin GP, Zhong X, et al. Rapid systematic review on risks and outcomes of sepsis: the influence of risk factors associated with health inequalities. Int J Equity Health. 2024;23(1):34. Available from: 10.1186/s12939-024-02114-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 97. Abel L, Casanova JL. Human determinants of age-dependent patterns of death from infection. Immunity. 2024;57(7):1457–65. Available from: 10.1016/j.immuni.2024.05.020 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 98. Fathi M, Markazi-Moghaddam N, Ramezankhani A. A systematic review on risk factors associated with sepsis in patients admitted to intensive care units. Aust Crit Care. 2019;32(2):155–64. Available from: 10.1016/j.aucc.2018.02.005 [ DOI ] [ PubMed ] [ Google Scholar ] 99. Branche A, Ramesh M, Francis B. A narrative review of key risk factors for severe illness following SARS-CoV-2, influenza virus, and respiratory syncytial virus infection. Infect Dis Ther. 2025;14 Suppl 1:39–61. Available from: 10.1007/s40121-024-01081-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 100. Alhamyani AH, Alamri MS, Aljuaid NW, Aloubthani AH, Alzahrani S, Alghamdi AA, et al. Sepsis in aging populations: a review of risk factors, diagnosis, and management. Cureus. 2024;16(12):e74973. Available from: 10.7759/cureus.74973 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 101. Varon B, Palacios-Baena ZR, de Kraker MEA, Rodríguez-Baño J, Leibovici L, Paul M. Universal risk factors for mortality in bloodstream infections (UNIFORM): a systematic review and Delphi survey. Clin Microbiol Infect. 2024;30(4):453–61. Available from: 10.1016/j.cmi.2023.12.030 [ DOI ] [ PubMed ] [ Google Scholar ] 102. Yang Y, Che K, Deng J, Tang X, Jing W, He X, et al. Assessing the impact of frailty on infection risk in older adults: prospective observational cohort study. JMIR Public Health Surveill. 2024;10:e59762. Available from: 10.2196/59762 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 103. Jain SK, Sheth S. Geriatric frailty [Internet]. PM&R KnowledgeNow; 2012. [updated 2025 May 8]. Available from: https://now.aapmr.org/geriatric-frailty/#:~:text=A%20recent%20meta%2Danalysis%20showed,as%20a%20standalone%20diagnostic%20tool 104. Trevisan C, Noale M, Amidei CB, Ferroni E, Basso C, Fedeli U, et al. Frailty and the risk of infection-related hospitalizations in older age: differences by sex. Maturitas. 2023;168:1–6. Available from: 10.1016/j.maturitas.2022.10.009 [ DOI ] [ PubMed ] [ Google Scholar ] 105. Iwai-Saito K, Shobugawa Y, Aida J, Kondo K. Frailty is associated with susceptibility and severity of pneumonia in older adults: a JAGES multilevel cross-sectional study. Sci Rep. 2021;11:7966. Available from: 10.1038/s41598-021-86854-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 106. Smeeth D, Warren-Gash C, Green RE, Butt J, Waterboer T, Hughes AD, et al. Associations of common infections with frailty and mortality in two UK cohort studies. medRxiv. 2025. Available from: 10.1101/2025.11.14.25340163 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 107. Ahmed SK, Hussein S, Qurbani K, Ibrahim RH, Fareeq A, Mahmood KA, et al. Antimicrobial resistance: impacts, challenges, and future prospects. J Med Surg Public Health. 2024;2:100081. Available from: 10.1016/j.glmedi.2024.100081 [ DOI ] [ Google Scholar ] 108. Chan YQ, Chen K, Chua GT, Wu P, Tung KTS, Tsang HW, et al. Risk factors for carriage of antimicrobial-resistant bacteria in community dwelling-children in the Asia-Pacific region: a systematic review and meta-analysis. JAC Antimicrob Resist. 2022;4(2):dlac036. Available from: 10.1093/jacamr/dlac036 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 109. Theodorakis N, Feretzakis G, Hitas C, Kreouzi M, Kalantzi S, Spyridaki A, et al. Antibiotic resistance in the elderly: mechanisms, risk factors, and solutions. Microorganisms. 2024;12(10):1978. Available from: 10.3390/microorganisms12101978 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 110. Blackmon S, Avendano E, Nirmala N, Chan CW, Morin RA, Balaji S, et al. Socioeconomic status and the risk for colonization or infection with priority bacterial pathogens: a global evidence map. Lancet Microbe. 2025;6(4):100993. Available from: 10.1016/j.lanmic.2024.100993 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 111. García-Parejo Y, González-Rubio J, García Guerrero J, Gómez-Suárez Sango A, Cantero-Escribano JM, Nájera A. Risk factors for colonisation by multidrug-resistant bacteria in critical care units. Intensive Crit Care Nurs. 2024;85:103760. Available from: 10.1016/j.iccn.2024.103760 [ DOI ] [ PubMed ] [ Google Scholar ] 112. Mendes Pedro D, Santos D, Meneses M, Gonçalves F, Domingos GJ, Caneiras C. Risk of colonization with multidrug-resistant Gram-negative bacteria among travellers and migrants: a narrative review. Trop Med Infect Dis. 2025;10(1):26. Available from: 10.3390/tropicalmed10010026 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 113. Laxminarayan R, Matsoso P, Pant S, Brower C, Røttingen JA, Klugman K, et al. Access to effective antimicrobials: a worldwide challenge. Lancet. 2016;387(10014):168–75. [ DOI ] [ PubMed ] [ Google Scholar ] 114. Samarasekera U. Ramanan Laxminarayan: seeking sustainable access to antibiotics. Lancet. 2024;403 (10442):2367. Available from: 10.1016/S0140-6736(24)01034-1 [ DOI ] [ PubMed ] [ Google Scholar ] 115. Laxminarayan R, Impalli I, Rangarajan R, Cohn J, Ramjeet K, Trainor B, et al. Expanding antibiotic, vaccine, and diagnostics development and access to tackle antimicrobial resistance. Lancet. 2024;403(10443):2534–50. [ DOI ] [ PubMed ] [ Google Scholar ] 116. Obolski U, Stein GY, Hadany L. Antibiotic restriction might facilitate the emergence of multi-drug resistance. PLoS Comput Biol. 2015;11(6):e1004340. Available from: 10.1371/journal.pcbi.1004340 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 117. Arcenillas P, Boix-Palop L, Gómez L, Xercavins M, March P, Martinez L, et al. Assessment of quality indicators for appropriate antibiotic use. Antimicrob Agents Chemother. 2018;62(12):e00875–18. Available from: 10.1128/AAC.00875-18. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 118. O’Riordan F, Shiely F, Byrne S, Fleming A. Quality indicators for hospital antimicrobial stewardship programmes: a systematic review. J Antimicrob Chemother. 2021;76(6):1406–19. Available from: 10.1093/jac/dkab034 [ DOI ] [ PubMed ] [ Google Scholar ] 119. Berry R, Catteau L, Caris MG, de Boer MGJ, Kherabi Y, Magrini E, et al. Identifying indicators for measuring appropriateness of antibiotic prescribing in high-income countries: a rapid systematic review. Clin Microbiol Infect. 2026:S1198-743X(26)00014-5. Available from: 10.1016/j.cmi.2026.01.011 [ DOI ] [ PubMed ] [ Google Scholar ] 120. Julián-Jiménez A, García DE, Del Castillo JG, Escalante-Kanashiro R, Candel González FJ, Merinos-Sánchez G, et al. Recommendations of the GT-LATINFURG for the identification and initial management of patients with severe infection and sepsis in emergency departments. Emergencias. 2026;38(1):27–63. Available from: 10.55633/s3me/005.2026 [ DOI ] [ PubMed ] [ Google Scholar ] 121. Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11): 1181–247. Available from: 10.1007/s00134-021-06506-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 122. Mensa J, Barberán J, Ferrer R, Borges M, Rascado P, Maseda E, et al. Recommendations for antibiotic selection for severe nosocomial infections. Rev Esp Quimioter. 2021;34(5):511–24. Available from: 10.37201/req/126.2021 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 123. Prescott HC, Antonelli M, Alhazzani W, Møller MH, Alshamsi F, Azevedo LCP, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock 2026. Crit Care Med. 2026. Available from: 10.1097/CCM.0000000000007075 [ DOI ] [ PubMed ] [ Google Scholar ] 124. Estella Á, Rello J. Optimal timing of antibiotic administration in septic patients: the need to reformulate this question. Eur J Intern Med. 2024;129:30–2. Available from: 10.1016/j.ejim.2024.08.016 [ DOI ] [ PubMed ] [ Google Scholar ] 125. Kollef MH, Shorr AF, Bassetti M, Timsit JF, Micek ST, Michelson AP, et al. Timing of antibiotic therapy in the ICU. Crit Care. 2021;25(1):360. Available from: 10.1186/s13054-021-03787-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 126. Rello J, Valenzuela-Sánchez F, Ruiz-Rodriguez M, Moyano S. Sepsis: a review of advances in management. Adv Ther. 2017;34(11):2393–411. Available from: 10.1007/s12325-017-0622-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 127. Díaz-González S, Martín-Conty JL, Pozo Vegas CD, López-Izquierdo R, Martín-Rodríguez F, Sanz-García A. Impact of point-of-care biomarkers on the improvement of the predictive capacity of early warning scores in prehospital care: a systematic review and meta-analysis. Emergencias. 2025;37(4):281–92. Available from: 10.55633/s3me/069.2025 [ DOI ] [ PubMed ] [ Google Scholar ] 128. Chalmers JD, Singanayagam A, Hill AT. C-reactive protein is an independent predictor of severity in community-acquired pneumonia. Am J Med. 2008;121(3):219–25. Available from: 10.1016/j.amjmed.2007.10.033 [ DOI ] [ PubMed ] [ Google Scholar ] 129. Luka S, Golea A, Tat RM, Lupan Mureșan EM, Voicescu GT, Vesa ȘC, et al. Biomarkers as predictors of mortality in sepsis and septic shock for patients admitted to emergency department: who is the winner? A prospective study. J Clin Med. 2024;13(19):5678. Available from: 10.3390/jcm13195678 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 130. Chen L, Zhang X, Shi P. Recent advances in biomarkers for detection and diagnosis of sepsis and organ dysfunction: a comprehensive review. Eur J Med Res. 2025;30:1081. Available from: 10.1186/s40001-025-03324-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 131. Varga NI, Benea AT, Suba MI, Bota AV, Avram CR, Boru C, et al. Predicting mortality in sepsis: the role of dynamic biomarker changes and clinical scores: a retrospective cohort study. Diagnostics (Basel). 2024;14(17):1973. Available from: 10.3390/diagnostics14171973 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 132. Julián-Jiménez A, Rubio-Díaz R, González Del Castillo J, Candel FJ. Nuevos modelos predictivos de bacteriemia en el servicio de urgencias: un paso adelante. Rev Esp Quimioter. 2022;35(4):344–56. Available from: 10.37201/req/015.2022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 133. Clemente C, Fuentes Ferrer ME, Ortega Heredia D, Julián Jiménez A, Martín-Sánchez FJ, González Del Castillo J. Usefulness of combining inflammatory biomarkers and clinical scales in an emergency department to stratify risk in patients with infections. Emergencias. 2024;36(1):9–16. Available from: 10.55633/s3me/04.2023 [ DOI ] [ PubMed ] [ Google Scholar ] 134. González del Castillo J, Del Toro E, Fragiel M, Oteo D, García-Álvarez A. Usefulness of monocyte distribution width (MDW) in enhancing diagnostic accuracy of infection among severely ill patients presenting to the emergency department. Rev Esp Quimioter. 2025;38 Suppl 1:5–10. Available from: 10.37201/req/s01.01.2025 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 135. Valeriani E, Falletta A, Pastori D, Porfidia A, Mastroianni CM, Di Bari S, et al. Midregional-proAdrenomedullin as a prognostic tool in sepsis and septic shock: a systematic review and meta-analysis. Eur J Clin Invest. 2024;54(9):e14225. Available from: 10.1111/eci.14225 [ DOI ] [ PubMed ] [ Google Scholar ] 136. Yáñez Palma MC, Roman F, Llopis-Roca F, Fragiel M, Julián Jiménez A, Martín-Sánchez FJ, et al. Utilidad de la región medial de la pro-adrenomodulina para la detección de bacteriemia verdadera en pacientes mayores atendidos en urgencias por sospecha de infección. Rev Esp Quimioter. 2024;37(1):78–87. Available from: 10.37201/req/110.2023 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 137. Yan MY, Gustad LT, Nytrø Ø. Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review. J Am Med Inform Assoc. 2022;29(3):559–75. Available from: 10.1093/jamia/ocab236 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 138. Estella Á, Armengol de la Hoz MA, González del Castillo J. Open data and artificial intelligence: a window of opportunity for septic patients in emergency departments. Emergencias. 2025;37(5):373–81. Available from: 10.55633/s3me/056.2025 [ DOI ] [ PubMed ] [ Google Scholar ] 139. Carbajo RS, Palma J, Martin-Loeches I. The next frontier in sepsis: connected ICU data for real-world clinical decision making. Intensive Care Med. 2026;52(2):301–8. Available from: 10.1007/s00134-025-08284-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 140. Goldschmidt E, Rannon E, Bernstein D, Wasserman A, Roimi M, Shrot A, et al. Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection. NPJ Digit Med. 2025;8(1):87. Available from: 10.1038/s41746-024-01426-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 141. Ramos-Casals M. Artificial intelligence in emergency medicine: from expectation to implementation. Emergencias. 2026;38(2):81–2. Available from: 10.55633/s3me/019.2026 [ DOI ] [ PubMed ] [ Google Scholar ] 142. De Waele JJ. Importance of timely and adequate source control in sepsis and septic shock. J Intensive Med. 2024;4(3):281–6. Available from: 10.1016/j.jointm.2024.01.002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 143. Karvellas CJ, Dong V, Abraldes JG, Lester EL, Kumar A. The impact of delayed source control and antimicrobial therapy in 196 patients with cholecystitis-associated septic shock: a cohort analysis. Can J Surg. 2019;62(3):189–98. Available from: 10.1503/cjs.009418 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 144. Boyd-Carson H, Doleman B, Cromwell D, Lockwood S, Williams JP, Tierney GM, et al. Delay in source control in perforated peptic ulcer leads to 6% increased risk of death per hour: a nationwide cohort study. World J Surg. 2020;44(3):869–75. Available from: 10.1007/s00268-019-05254-x [ DOI ] [ PubMed ] [ Google Scholar ] 145. Reitz KM, Kennedy J, Li SR, Handzel R, Tonetti DA, Neal MD, et al. Association between time to source control in sepsis and 90-day mortality. JAMA Surg. 2022;157(9):817–26. Available from: 10.1001/jamasurg.2022.2761 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 146. Scheer CS, Annane D, Artigas A, Linder A, Sander M, Demirkiran O, et al. Infrastructure and current practice of blood culture diagnostics in patients with sepsis: teachings and recommendations from the European Sepsis Care Survey. Crit Care. 2025;29(1):345. Available from: 10.1186/s13054-025-05582-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 147. Oudiane L, Benyahia M, Salipante F, Dubois A, Muller L, Lavigne JP, et al. Clinical impact of the BCID2 and rapid AST VITEK REVEAL on antibiotic optimisation in critically ill patients with Gram-negative bloodstream infections: a quasi-experimental pre/post interventional study. J Antimicrob Chemother. 2025;80(10):2665–75. Available from: 10.1093/jac/dkaf271 [ DOI ] [ PubMed ] [ Google Scholar ] 148. Baron EJ, Miller JM, Weinstein MP, Richter SS, Gilligan PH, Thomson RB Jr, et al. A guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2013 recommendations by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM). Clin Infect Dis. 2013;57(4):e22–121. Available from: 10.1093/cid/cit278 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 149. Lamy B, Dargère S, Arendrup MC, Parienti JJ, Tattevin P. How to optimize the use of blood cultures for the diagnosis of bloodstream infections? A state-of-the art. Front Microbiol. 2016;7:697. Available from: 10.3389/fmicb.2016.00697 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 150. Vaughn VM, Dickson RP, Horowitz JK, Flanders SA. Community-acquired pneumonia: a review. JAMA. 2024;332(15):1282–95. Available from: 10.1001/jama.2024.14796 [ DOI ] [ PubMed ] [ Google Scholar ] 151. Buchan BW, Windham S, Balada-Llasat JM, Leber A, Harrington A, Relich R, et al. Practical comparison of the BioFire FilmArray Pneumonia Panel to routine diagnostic methods and potential impact on antimicrobial stewardship in adult hospitalized patients with lower respiratory tract infections. J Clin Microbiol. 2020;58(7):e00135–20. Available from: 10.1128/JCM.00135-20 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 152. Candel FJ, Salavert M, Cantón R, Del Pozo JL, Galán-Sánchez F, Navarro D, et al. The role of rapid multiplex molecular syndromic panels in the clinical management of infections in critically ill patients: an experts-opinion document. Crit Care. 2024;28(1):440. Available from: 10.1186/s13054-024-05224-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 153. Giménez E, Clari MÁ, Albert E, Carbonell N, Navarro D. Performance of the BIOFIRE FilmArray Pneumonia Plus Panel for quantifiable bacterial targets compared to semiquantitative culture methods: a systematic review and meta-analysis. J Infect. 2026;92(2):106683. Available from: 10.1016/j.jinf.2026.106683 [ DOI ] [ PubMed ] [ Google Scholar ] 154. Tansarli GS, Chapin KC. Diagnostic test accuracy of the BioFire FilmArray meningitis/encephalitis panel: a systematic review and meta-analysis. Clin Microbiol Infect. 2020;26(3):281–90. Available from: 10.1016/j.cmi.2019.11.016 [ DOI ] [ PubMed ] [ Google Scholar ] 155. Leber AL, Everhart K, Balada-Llasat JM, Cullison J, Daly J, Holt S, et al. Multicenter evaluation of BioFire FilmArray meningitis/encephalitis panel for detection of bacteria, viruses, and yeast in cerebrospinal fluid specimens. J Clin Microbiol. 2016;54(9):2251–61. Available from: 10.1128/JCM.00730-16 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 156. Hanson KE, Slechta ES, Killpack JA, Heyrend C, Lunt T, Daly JA, et al. Preclinical assessment of a fully automated multiplex PCR panel for detection of central nervous system pathogens. J Clin Microbiol. 2016;54(3):785–7. Available from: 10.1128/JCM.02850-15 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 157. Liesman RM, Strasburg AP, Heitman AK, Theel ES, Patel R, Binnicker MJ. Evaluation of a commercial multiplex molecular panel for diagnosis of infectious meningitis and encephalitis. J Clin Microbiol. 2018;56(4):e01927–17. Available from: 10.1128/JCM.01927-17 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 158. Messacar K, Parker SK, Todd JK, Dominguez SR. Implementation of rapid molecular infectious disease diagnostics: the role of diagnostic and antimicrobial stewardship. J Clin Microbiol. 2017;55(3):715–23. Available from: 10.1128/JCM.02264-16 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 159. Rabin EE, Walter JM, Wunderink RG, Qi C, Pickens CI. Clinical significance of culture-negative, PCR-positive bronchoalveolar lavage results in severe pneumonia. ERJ Open Res. 2023;9(6):00343–2023. Available from: 10.1183/23120541.00343-2023 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 160. Clerc O, Prod’hom G, Vogne C, Bizzini A, Calandra T, Greub G. Impact of matrix-assisted laser desorption ionization time-of-flight mass spectrometry on the clinical management of patients with Gram-negative bacteremia: a prospective observational study. Clin Infect Dis. 2013;56(8):1101–7. Available from: 10.1093/cid/cis1204 [ DOI ] [ PubMed ] [ Google Scholar ] 161. Perez KK, Olsen RJ, Musick WL, Cernoch PL, Davis JR, Peterson LE, et al. Integrating rapid diagnostics and antimicrobial stewardship improves outcomes in patients with antibiotic-resistant Gram-negative bacteremia. J Infect. 2014;69(3):216–25. Available from: 10.1016/j.jinf.2014.05.005 [ DOI ] [ PubMed ] [ Google Scholar ] 162. Idelevich EA, Becker K. How to accelerate antimicrobial susceptibility testing. Clin Microbiol Infect. 2019;25(11):1347–55. Available from: 10.1016/j.cmi.2019.04.025 [ DOI ] [ PubMed ] [ Google Scholar ] 163. MacVane SH, Nolte FS. Benefits of adding a rapid PCR-based blood culture identification panel to an established antimicrobial stewardship program. J Clin Microbiol. 2016;54(10):2455–63. Available from: 10.1128/JCM.00996-16 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 164. Timbrook TT, Morton JB, McConeghy KW, Caffrey AR, Mylonakis E, LaPlante KL. The effect of molecular rapid diagnostic testing on clinical outcomes in bloodstream infections: a systematic review and meta-analysis. Clin Infect Dis. 2017;64(1):15–23. Available from: 10.1093/cid/ciw649 [ DOI ] [ PubMed ] [ Google Scholar ] 165. Rotundo S, Russo A, Morena R, Garofalo E, Morrone HL, Mazza G, et al. Early targeted therapy guided by rapid phenotypic antimicrobial susceptibility testing in critically ill patients with Gram-negative bacterial bloodstream infections: a retrospective cohort study. J Antimicrob Chemother. 2026;81(3):dkag056. Available from: 10.1093/jac/dkag056 [ DOI ] [ PubMed ] [ Google Scholar ] 166. MacVane SH, Dwivedi HP. Evaluating the impact of rapid antimicrobial susceptibility testing for bloodstream infections: a review of actionability, antibiotic use and patient outcome metrics. J Antimicrob Chemother. 2024;79 Suppl 2:i13–25. Available from: 10.1093/jac/dkae282 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 167. Charalampous T, Kay GL, Richardson H, Aydin A, Baldan R, Jeanes C, et al. Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection. Nat Biotechnol. 2019;37(7):783–92. Available from: 10.1038/s41587-019-0156-5 [ DOI ] [ PubMed ] [ Google Scholar ] 168. Hogan CA, Yang S, Garner OB, Green DA, Gomez CA, Dien Bard J, et al. Clinical impact of metagenomic next-generation sequencing of plasma cell-free DNA for the diagnosis of infectious diseases: a multicenter retrospective cohort study. Clin Infect Dis. 2021;72(2):239–45. Available from: 10.1093/cid/ciaa035 [ DOI ] [ PubMed ] [ Google Scholar ] 169. Blauwkamp TA, Thair S, Rosen MJ, Blair L, Lindner MS, Vilfan ID, et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat Microbiol. 2019;4(4):663–74. Available from: 10.1038/s41564-018-0349-6 [ DOI ] [ PubMed ] [ Google Scholar ] 170. Livermore DM, Woodford N. Carbapenemases: a problem in waiting? Curr Opin Microbiol. 2000;3(5):489–95. Available from: 10.1016/s1369-5274(00)00128-4 [ DOI ] [ PubMed ] [ Google Scholar ] 171. De Waele JJ, Akova M, Antonelli M, Canton R, Carlet J, De Backer D, et al. Antimicrobial resistance and antibiotic stewardship programs in the ICU: insistence and persistence in the fight against resistance. A position statement from ESICM/ESCMID/WAAAR round table on multi-drug resistance. Intensive Care Med. 2018;44(2): 189–96. Available from: 10.1007/s00134-017-5036-1 [ DOI ] [ PubMed ] [ Google Scholar ] 172. Fabre V, Sharara SL, Salinas AB, Carroll KC, Desai S, Cosgrove SE. Does this patient need blood cultures? A scoping review of indications for blood cultures in adult nonneutropenic inpatients. Clin Infect Dis. 2020;71(5):1339–47. Available from: 10.1093/cid/ciaa039 [ DOI ] [ PubMed ] [ Google Scholar ] 173. Bou G, Cantón R, Martínez-Martínez L, Navarro D, Vila J. Fundamentals and implementation of microbiological diagnostic stewardship programs. Enferm Infecc Microbiol Clin (Engl Ed). 2021;39(5):248–51. Available from: 10.1016/j.eimc.2020.02.019 [ DOI ] [ PubMed ] [ Google Scholar ] 174. Banerjee R, Humphries R. Clinical and laboratory considerations for the rapid detection of carbapenem-resistant Enterobacteriaceae. Virulence. 2017;8(4):427–39. Available from: 10.1080/21505594.2016.1185577 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 175. Singh HK, Claeys KC, Advani SD, Ballam YJ, Penney J, Schutte KM, et al. Diagnostic stewardship to improve patient outcomes and healthcare-associated infection (HAI) metrics. Infect Control Hosp Epidemiol. 2024;45(4):405–11. Available from: 10.1017/ice.2023.284 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 176. Liberati C, Donà D, Maestri L, Petris MG, Barbieri E, Gallo E, et al. Application of the Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the empiric antibiotic treatment of febrile neutropenia in oncological paediatric patients: experience from two paediatric hospitals in Northern Italy. Ann Clin Microbiol Antimicrob. 2024;23(1):16. Available from: 10.1186/s12941-024-00673-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 177. Ramasco F, Méndez R, Suarez de la Rica A, González de Castro R, Maseda E. Sepsis stewardship: the puzzle of antibiotic therapy in the context of individualization of decision making. J Pers Med. 2024;14(1):106. Available from: 10.3390/jpm14010106 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 178. Kumar A, Roberts D, Wood KE, Light B, Parrillo JE, Sharma S, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med. 2006;34(6):1589–96. Available from: 10.1097/01.CCM.0000217961.75225.E9 [ DOI ] [ PubMed ] [ Google Scholar ] 179. Lodise T, Berger A, Altincatal A, Wang R, Bhagnani T, Gillard P, et al. Antimicrobial resistance or delayed appropriate therapy: does one influence outcomes more than the other among patients with serious infections due to carbapenem-resistant versus carbapenem-susceptible Enterobacteriaceae? Open Forum Infect Dis. 2019;6(6):ofz194. Available from: 10.1093/ofid/ofz194 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 180. Seymour CW, Gesten F, Prescott HC, Friedrich ME, Iwashyna TJ, Phillips GS, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med. 2017;376(23):2235–44. Available from: 10.1056/NEJMoa1703058 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 181. Andersson M, Östholm-Balkhed Å, Fredrikson M, Holmbom M, Hällgren A, Berg S, et al. Delay of appropriate antibiotic treatment is associated with high mortality in patients with community-onset sepsis in a Swedish setting. Eur J Clin Microbiol Infect Dis. 2019;38(7):1223–34. Available from: 10.1007/s10096-019-03529-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 182. Siewers K, Abdullah SMOB, Sørensen RH, Nielsen FE. Time to administration of antibiotics and mortality in sepsis. J Am Coll Emerg Physicians Open. 2021;2(3):e12435. Available from: 10.1002/emp2.12435 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 183. Van Heuverswyn J, Valik JK, van der Werff SD, Hedberg P, Giske C, Nauclér P. Association between time to appropriate antimicrobial treatment and 30-day mortality in patients with bloodstream infections: a retrospective cohort study. Clin Infect Dis. 2023;76(3):469–78. Available from: 10.1093/cid/ciac727 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 184. Tang F, Yuan H, Li X, Qiao L. Effect of delayed antibiotic use on mortality outcomes in patients with sepsis or septic shock: a systematic review and meta-analysis. Int Immunopharmacol. 2024;129:111616. Available from: 10.1016/j.intimp.2024.111616 [ DOI ] [ PubMed ] [ Google Scholar ] 185. Philippon AL, Lebal S, Cancella de Abreu M, Gerlier C, Mirò O, Simon T, et al. Association between time to antibiotic and mortality in patients with suspected sepsis in the Emergency Department: post hoc analysis of the 1-BED randomized clinical trial. Eur J Emerg Med. 2025;32(2):109–15. Available from: 10.1097/MEJ.0000000000001212 [ DOI ] [ PubMed ] [ Google Scholar ] 186. López Medrano F, Alfayate S, Carratalà J, Chamorro-Camazón J, Cordero E, Cruz-Cañete M, et al. Executive summary. Diagnosis, treatment and prophylaxis of influenza virus infection. Enferm Infecc Microbiol Clin (Engl Ed). 2023;41(2):111–22. Available from: 10.1016/j.eimce.2022.10.013 [ DOI ] [ PubMed ] [ Google Scholar ] 187. Rosa RG, Goldani LZ. Cohort study of the impact of time to antibiotic administration on mortality in patients with febrile neutropenia. Antimicrob Agents Chemother. 2014;58(7):3799–803. Available from: 10.1128/AAC.02561-14 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 188. Kadri SS, Lai YL, Warner S, Strich JR, Babiker A, Ricotta EE, et al. ; National Institutes of Health Antimicrobial Resistance Outcomes Research Initiative (NIH-ARORI) . Inappropriate empirical antibiotic therapy for bloodstream infections based on discordant in-vitro susceptibilities: a retrospective cohort analysis of prevalence, predictors, and mortality risk in US hospitals. Lancet Infect Dis. 2021;21(2):241–51. Available from: 10.1016/S1473-3099(20)30477-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 189. Zilberberg MD, Nathanson BH, Sulham K, Fan W, Shorr AF. Carbapenem resistance, inappropriate empiric treatment and outcomes among patients hospitalized with Enterobacteriaceae urinary tract infection, pneumonia and sepsis. BMC Infect Dis. 2017;17(1):279. Available from: 10.1186/s12879-017-2383-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 190. Tumbarello M, Trecarichi EM, De Rosa FG, Giannella M, Giacobbe DR, Bassetti M, et al. ; ISGRI-SITA . Infections caused by KPC-producing Klebsiella pneumoniae: differences in therapy and mortality in a multicentre study. J Antimicrob Chemother. 2015;70(7):2133–43. Available from: 10.1093/jac/dkv086 [ DOI ] [ PubMed ] [ Google Scholar ] 191. Retamar P, Portillo MM, López-Prieto MD, Rodríguez-López F, de Cueto M, García MV, et al. Impact of inadequate empirical therapy on the mortality of patients with bloodstream infections: a propensity score-based analysis. Antimicrob Agents Chemother. 2012;56(1):472–8. Available from: 10.1128/AAC.00462-11 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 192. Zilberberg MD, Shorr AF, Micek ST, Vazquez-Guillamet C, Kollef MH. Multi-drug resistance, inappropriate initial antibiotic therapy and mortality in Gram-negative severe sepsis and septic shock: a retrospective cohort study. Crit Care. 2014;18(6):596. Available from: 10.1186/s13054-014-0596-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 193. Marquet K, Liesenborgs A, Bergs J, Vleugels A, Claes N. Incidence and outcome of inappropriate in-hospital empiric antibiotics for severe infection: a systematic review and meta-analysis. Crit Care. 2015;19(1):63. Available from: 10.1186/s13054-015-0795-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 194. Pasquau-Liaño J, Sadyrbaeva-Dolgova S, Sequera-Arquellada S, García-Vallecillos C, Hidalgo-Tenorio C. Timing in antibiotic therapy: when and how to start, de-escalate and stop antibiotic therapy. Proposals from a stablished antimicrobial stewardship program. Rev Esp Quimioter. 2022;35 Suppl 3:102–7. Available from: 10.37201/req/s03.22.2022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 195. Girón Ortega JA, Retamar P, Pérez Galera SI, Martínez Pérez-Crespo P, López-Cortés LE, Poyatos L, et al. Predictores de inadecuación de tratamiento antibiótico empírico en pacientes muy ancianos con bacteriemias. Cohorte PROBAC. CO#28 en XXIII Congreso Nacional de la Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica (SEIMC), Madrid 2019. Enferm Infecc Microbiol Clin. 2019;37(Espec Cong 1):26–7. ISSN: 0213-005X, 1578-1852. [ Google Scholar ] 196. Retamar P, López-Prieto MD, Rodríguez-López F, de Cueto M, García MV, González-Galan V, et al. ; SAEI/SAMPAC/REIPI Bacteremia Group . Predictors of early mortality in very elderly patients with bacteremia: a prospective multicenter cohort. Int J Infect Dis. 2014;26:83–7. Available from: 10.1016/j.ijid.2014.04.029 [ DOI ] [ PubMed ] [ Google Scholar ] 197. Rosa Riestra Sdl, Martínez Pérez-Crespo PM, Pérez Rodríguez MT, Sousa A, Goikoetxea J, Reguera Iglesias JM, et al. Mortality impact of further delays in active targeted antibiotic therapy in bacteraemic patients that did not receive initial active empiric treatment: results from the prospective, multicentre cohort PROBAC. Int J Infect Dis. 2024;145:107072. Available from: 10.1016/j.ijid.2024.107072 [ DOI ] [ PubMed ] [ Google Scholar ] 198. Honda H, Higuchi N, Shintani K, Higuchi M, Warren DK. Inadequate empiric antimicrobial therapy and mortality in geriatric patients with bloodstream infection: a target for antimicrobial stewardship. J Infect Chemother. 2018;24(10):807–11. Available from: 10.1016/j.jiac.2018.06.009 [ DOI ] [ PubMed ] [ Google Scholar ] 199. Motos-Bescós M, Ruiz-Ramos J, Agra-Montava I, Moliné-Pareja A, Palacio-Alvarez P, Villarejo-Jiménez A, et al. Factores de riesgo asociados a inadecuación del tratamiento empírico en pacientes con sepsis y bacteremia atendidos en un servicio de urgencias. Rev Esp Quimioter. 2026;39(1):30–8. Available from: 10.37201/req/101.2025 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 200. Gudiol C, Ruiz-Camps I, Puerta-Alcalde P, Abdala E, Oltolni C, Ayaz MC, et al. Impacto de la resistencia antibiótica en los pacientes neutropénicos con cáncer y bacteriemia por Pseudomonas aeruginosa (Estudio IRONIC). CO#75 en XXIII Congreso Nacional de la Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica (SEIMC), Madrid 2019. Enferm Infecc Microbiol Clin. 2019;37(Espec Cong 1):50–1. ISSN: 0213-005X, 1578-1852. [ Google Scholar ] 201. Dietl B, Boix-Palop L, Gisbert L, Mateu A, Garreta G, Xercavins M, et al. Risk factors associated with inappropriate empirical antimicrobial treatment in bloodstream infections. A cohort study. Front Pharmacol. 2023;14:1132530. Available from: 10.3389/fphar.2023.1132530 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 202. Kim HS, Park BK, Kim SK, Han SB, Lee JW, Lee DG, et al. Clinical characteristics and outcomes of Pseudomonas aeruginosa bacteremia in febrile neutropenic children and adolescents with the impact of antibiotic resistance: a retrospective study. BMC Infect Dis. 2017;17:500. Available from: 10.1186/s12879-017-2597-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 203. Olivares-Navarro P, Pérez-Rodríguez MT, Sousa A, Goikoetxea-Agirre AJ, Blanco Vidal MJ, Plata A, et al. ; PROBAC/GEIRAS-SEIMC/SAMICEI . Mortality risk of ESBL producers in Escherichia coli bacteraemia: a comprehensive analysis using the PROBAC cohort. J Antimicrob Chemother. 2026;81(5):dkag130. Available from: 10.1093/jac/dkag130 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 204. Bassetti M, Rello J, Blasi F, Goossens H, Sotgiu G, Tavoschi L, et al. Systematic review of the impact of appropriate versus inappropriate initial antibiotic therapy on outcomes of patients with severe bacterial infections. Int J Antimicrob Agents. 2020;56(6):106184. Available from: 10.1016/j.ijantimicag.2020.106184 [ DOI ] [ PubMed ] [ Google Scholar ] 205. Ohnuma T, Chihara S, Costin B, Treggiari MM, Bartz RR, Raghunathan K, et al. Association of appropriate empirical antimicrobial therapy with in-hospital mortality in patients with bloodstream infections in the US. JAMA Netw Open. 2023;6(1):e2249353. Available from: 10.1001/jamanetworkopen.2022.49353 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 206. Gallardo-Pizarro A, Teijón-Lumbreras C, Aiello TF, Monzó-Gallo P, Martinez-Urrea A, Terrones-Campos C, et al. Towards personalised empirical antibiotic therapy in febrile neutropenia: a theoretical model based on machine learning and prior colonisation with multidrug-resistant gram-negative bacilli: a retrospective proof-of-concept cohort study. Ther Adv Infect Dis. 2026;13:20499361251411061. Available from: 10.1177/20499361251411061 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 207. Gould IM. Determinants of response to antibiotic therapy. J Chemother. 1998;10(5):347–53. Available from: 10.1179/joc.1998.10.5.347 [ DOI ] [ PubMed ] [ Google Scholar ] 208. Alikhani MS, Nazari M, Hatamkhani S. Enhancing antibiotic therapy through comprehensive pharmacokinetic/pharmacodynamic principles. Front Cell Infect Microbiol. 2025;15:1521091. Available from: 10.3389/fcimb.2025.1521091 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 209. Connors K, Kuti JL, Nicolau DP. Optimizing antibiotic pharmacodynamics for clinical practice. Pharm Anal Acta. 2013;4:214. Available from: 10.4172/2153-2435.1000214 [ DOI ] [ Google Scholar ] 210. Patel K, Bunachita S, Agarwal AA, Bhamidipati A, Patel UK. A comprehensive overview of antibiotic selection and the factors affecting it. Cureus. 2021;13(3):e13925. Available from: 10.7759/cureus.13925 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 211. Li J, Xie S, Ahmed S, Wang F, Gu Y, Zhang C, et al. Antimicrobial activity and resistance: influencing factors. Front Pharmacol. 2017;8:364. Available from: 10.3389/fphar.2017.00364 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 212. Yang JH, Bening SC, Collins JJ. Antibiotic efficacy: context matters. Curr Opin Microbiol. 2017;39:73–80. Available from: 10.1016/j.mib.2017.09.002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 213. Alexandersen NR, Nielsen KL, Häussler S, Bjarnsholt T, Schønning K. Antibiotic tolerance and persistence in clinical isolates of Escherichia coli evaluated by high-resolution time-kill assays. Microbiol Spectr. 2025;13:e01124–25. Available from: 10.1128/spectrum.01124-25 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 214. Ishak A, Mazonakis N, Spernovasilis N, Akinosoglou K, Tsioutis C. Bactericidal versus bacteriostatic antibacterials: clinical significance, differences and synergistic potential in clinical practice. J Antimicrob Chemother. 2025;80(1):1–17. Available from: 10.1093/jac/dkae380 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 215. Maldonado N, López-Hernández I, López-Cortés LE, Martínez Pérez-Crespo PM, Retamar-Gentil P, García-Montaner A, et al. ; PROBAC REIPI/GEIRAS-SEIMC/SAMICEI Group . Association of microbiological factors with mortality in Escherichia coli bacteraemia presenting with sepsis/septic shock: a prospective cohort study. Clin Microbiol Infect. 2024;30(8):1035–41. Available from: 10.1016/j.cmi.2024.04.001 [ DOI ] [ PubMed ] [ Google Scholar ] 216. Tamma PD, Han JH, Rock C, Harris AD, Lautenbach E, Hsu AJ, et al. ; Antibacterial Resistance Leadership Group . Carbapenem therapy is associated with improved survival compared with piperacillin-tazobactam for patients with extended-spectrum β-lactamase bacteremia. Clin Infect Dis. 2015;60(9):1319–25. Available from: 10.1093/cid/civ003 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 217. Harris PNA, Tambyah PA, Lye DC, Mo Y, Lee TH, Yilmaz M, et al. ; MERINO Trial Investigators and the Australasian Society for Infectious Disease Clinical Research Network (ASID-CRN) . Effect of piperacillin-tazobactam vs meropenem on 30-day mortality for patients with E coli or Klebsiella pneumoniae bloodstream infection and ceftriaxone resistance: a randomized clinical trial. JAMA. 2018;320(10):984–94. Available from: 10.1001/jama.2018.12163 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 218. Ofer-Friedman H, Shefler C, Sharma S, Tirosh A, Tal-Jasper R, Kandipalli D, et al. Carbapenems versus piperacillin-tazobactam for bloodstream infections of nonurinary source caused by extended-spectrum beta-lactamase-producing Enterobacteriaceae. Infect Control Hosp Epidemiol. 2015;36(8):981–5. Available from: 10.1017/ice.2015.101 [ DOI ] [ PubMed ] [ Google Scholar ] 219. Tamma PD, Heil EL, Justo JA, Mathers AJ, Satlin MJ, Bonomo RA. Infectious Diseases Society of America 2024 guidance on the treatment of antimicrobial-resistant Gram-negative infections. Clin Infect Dis. 2024:ciae403. Available from: 10.1093/cid/ciae403 [ DOI ] [ PubMed ] [ Google Scholar ] 220. Fernández Guerrero ML, de Górgolas M. Comparative activity of cloxacillin and vancomycin against methicillin-susceptible Staphylococcus aureus experimental endocarditis. J Antimicrob Chemother. 2006;58(5):1066–9. Available from: 10.1093/jac/dkl355 [ DOI ] [ PubMed ] [ Google Scholar ] 221. McDanel JS, Perencevich EN, Diekema DJ, Herwaldt LA, Smith TC, Chrischilles EA, et al. Comparative effectiveness of beta-lactams versus vancomycin for treatment of methicillin-susceptible Staphylococcus aureus bloodstream infections among 122 hospitals. Clin Infect Dis. 2015;61(3):361–7. Available from: 10.1093/cid/civ308 [ DOI ] [ PubMed ] [ Google Scholar ] 222. Mitaka H, Miyashita S, Yamada T, Satoi S, Chernyavsky. Effectiveness of beta-lactams versus vancomycin for treatment of methicillin-susceptible Staphylococcus aureus bacteremia: a systematic review and meta-analysis. J Sci Innov Med. 2019;2(2):14. Available from: 10.29024/jsim.26 [ DOI ] [ Google Scholar ] 223. Yu G, Lou Y, He L, Wang Z, Ren Y. β-lactam antibiotics vs vancomycin in treating methicillin-sensitive Staphylococcus aureus bloodstream infections: a meta-analysis of clinical outcomes. Eur J Clin Microbiol Infect Dis. 2025;44(9):2019–31. Available from: 10.1007/s10096-025-05164-y [ DOI ] [ PubMed ] [ Google Scholar ] 224. Claeys KC, Zasowski EJ, Casapao AM, Lagnf AM, Nagel JL, Nguyen CT, et al. Daptomycin improves outcomes regardless of vancomycin MIC in a propensity-matched analysis of methicillin-resistant Staphylococcus aureus bloodstream infections. Antimicrob Agents Chemother. 2016;60(10):5841–8. Available from: 10.1128/AAC.00227-16 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 225. Maraolo AE, Giaccone A, Gentile I, Saracino A, Bavaro DF. Daptomycin versus vancomycin for the treatment of methicillin-resistant Staphylococcus aureus bloodstream infection with or without endocarditis: a systematic review and meta-analysis. Antibiotics (Basel). 2021;10(8):1014. Available from: 10.3390/antibiotics10081014 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 226. Schweizer ML, Richardson K, Vaughan Sarrazin MS, Goto M, Livorsi DJ, Nair R, et al. Comparative effectiveness of switching to daptomycin versus remaining on vancomycin among patients with methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections. Clin Infect Dis. 2021;72 Suppl 1:S68–73. Available from: 10.1093/cid/ciaa1572 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 227. Samura M, Kitahiro Y, Tashiro S, Moriyama H, Hamamura Y, Takahata I, et al. Efficacy and safety of daptomycin versus vancomycin for bacteremia caused by methicillin-resistant Staphylococcus aureus with vancomycin minimum inhibitory concentration >1 μg/mL: a systematic review and meta-analysis. Pharmaceutics. 2022;14(4):714. Available from: 10.3390/pharmaceutics14040714 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 228. Adamu Y, Puig-Asensio M, Dabo B, Schweizer ML. Comparative effectiveness of daptomycin versus vancomycin among patients with methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections: a systematic literature review and meta-analysis. PLoS One. 2024;19(2):e0293423. Available from: 10.1371/journal.pone.0293423 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 229. Mascio CT, Alder JD, Silverman JA. Bactericidal action of daptomycin against stationary-phase and nondividing Staphylococcus aureus cells. Antimicrob Agents Chemother. 2007;51(12):4255–60. Available from: 10.1128/AAC.00824-07 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 230. McKay GA, Beaulieu S, Arhin FF, Belley A, Sarmiento I, Parr T Jr, et al. Time-kill kinetics of oritavancin and comparator agents against Staphylococcus aureus , Enterococcus faecalis and Enterococcus faecium . J Antimicrob Chemother. 2009;63(6):1191–9. Available from: 10.1093/jac/dkp126 [ DOI ] [ PubMed ] [ Google Scholar ] 231. Huang YT, Liao CH, Teng LJ, Hsueh PR. Comparative bactericidal activities of daptomycin, glycopeptides, linezolid and tigecycline against blood isolates of Gram-positive bacteria in Taiwan. Clin Microbiol Infect. 2008;14(2):124–9. Available from: 10.1111/j.1469-0691.2007.01888.x [ DOI ] [ PubMed ] [ Google Scholar ] 232. Herrera M, Mobilia L, Posse G, Limansky A, Ballerini V, Bantar C. Comparative time-kill study of doxycycline, tigecycline, cefazolin and vancomycin against several clones of Staphylococcus aureus . Curr Clin Pharmacol. 2013;8(4):332–9. Available from: 10.2174/15748847113089990063 [ DOI ] [ PubMed ] [ Google Scholar ] 233. Clouse FL, Hovde LB, Rotschafer JC. In vitro evaluation of the activities of telavancin, cefazolin, and vancomycin against methicillin-susceptible and methicillin-resistant Staphylococcus aureus in peritoneal dialysate. Antimicrob Agents Chemother. 2007;51(12):4521–4. Available from: 10.1128/AAC.00435-07 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 234. Gerges B, Rolston K, Shelburne SA, Rosenblatt J, Prince R, Raad I. The in vitro activity of delafloxacin and comparator agents against bacterial pathogens isolated from patients with cancer. JAC Antimicrob Resist. 2023;5(2):dlad034. Available from: 10.1093/jacamr/dlad034 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 235. Steed ME, Vidaillac C, Winterfield P, Biek D, Rybak MJ. Evaluation of ceftaroline activity versus ceftriaxone against clinical isolates of Streptococcus pneumoniae with various susceptibilities to cephalosporins in an in vitro pharmacokinetic/pharmacodynamic model. Antimicrob Agents Chemother. 2012;56(5):2691–5. Available from: 10.1128/AAC.06185-11 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 236. Zhong NS, Sun T, Zhuo C, D’Souza G, Lee SH, Lan NH, et al. Ceftaroline fosamil versus ceftriaxone for the treatment of Asian patients with community-acquired pneumonia: a randomised, controlled, double-blind, phase 3, non-inferiority with nested superiority trial. Lancet Infect Dis. 2015;15(2):161–71. Available from: 10.1016/S1473-3099(14)71018-7 [ DOI ] [ PubMed ] [ Google Scholar ] 237. Taboada M, Melnick D, Iaconis JP, Sun F, Zhong NS, File TM, et al. Ceftaroline fosamil versus ceftriaxone for the treatment of community-acquired pneumonia: individual patient data meta-analysis of randomized controlled trials. J Antimicrob Chemother. 2016;71(4):862–70. Available from: 10.1093/jac/dkv415 [ DOI ] [ PubMed ] [ Google Scholar ] 238. Rosanova MT, Aguilar PS, Sberna N, Lede R. Efficacy and safety of ceftaroline: systematic review and meta-analysis. Ther Adv Infect Dis. 2018;6:2049936118808655. Available from: 10.1177/2049936118808655 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 239. Lan SH, Chang SP, Lai CC, Lu LC, Chao CM. Efficacy and safety of ceftaroline for the treatment of community-acquired pneumonia: a systemic review and meta-analysis of randomized controlled trials. J Clin Med. 2019;8(6):824. Available from: 10.3390/jcm8060824 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 240. Zhuo C, Huang Y, Liu W, Xu JF, Zhu WY, Stone GG, et al. Efficacy and safety of ceftaroline fosamil in hospitalized patients with community-acquired pneumonia in China: subset analysis of an international phase 3 randomized controlled trial. Infect Drug Resist. 2022;15:605–17. Available from: 10.2147/IDR.S342558 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 241. Lenhard JR, Bulman ZP. Inoculum effect of β-lactam antibiotics. J Antimicrob Chemother. 2019;74(10):2825–43. Available from: 10.1093/jac/dkz226 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 242. DeRyke CA, Banevicius MA, Fan HW, Nicolau DP. Bactericidal activities of meropenem and ertapenem against extended-spectrum-beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae in a neutropenic mouse thigh model. Antimicrob Agents Chemother. 2007;51(4):1481–6. Available from: 10.1128/AAC.00752-06 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 243. Maglio D, Ong C, Banevicius MA, Geng Q, Nightingale CH, Nicolau DP. Determination of the in vivo pharmacodynamic profile of cefepime against extended-spectrum-beta-lactamase-producing Escherichia coli at various inocula. Antimicrob Agents Chemother. 2004;48(6):1941–7. Available from: 10.1128/AAC.48.6.1941-1947.2004 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 244. Bronner S, Murbach V, Peter JD, Levêque D, Elkhaïli H, Salmon Y, et al. Ex vivo pharmacodynamics of amoxicillin-clavulanate against beta-lactamase-producing Escherichia coli in a Yucatan miniature pig model that mimics human pharmacokinetics. Antimicrob Agents Chemother. 2002;46(12):3782–9. Available from: 10.1128/AAC.46.12.3782-3789.2002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 245. Tam VH, Gamez EA, Weston JS, Gerard LN, Larocco MT, Caeiro JP, et al. Outcomes of bacteremia due to Pseudomonas aeruginosa with reduced susceptibility to piperacillin-tazobactam: implications on the appropriateness of the resistance breakpoint. Clin Infect Dis. 2008;46(6):862–7. Available from: 10.1086/528712 [ DOI ] [ PubMed ] [ Google Scholar ] 246. Edwards SJ, Clarke MJ, Wordsworth S, Welton NJ. Carbapenems versus other beta-lactams in the treatment of hospitalised patients with infection: a mixed treatment comparison. Curr Med Res Opin. 2009;25(1):251–61. Available from: 10.1185/03007990802633160 [ DOI ] [ PubMed ] [ Google Scholar ] 247. Taccone FS, Laterre PF, Dugernier T, Spapen H, Delattre I, Wittebole X, et al. Insufficient β-lactam concentrations in the early phase of severe sepsis and septic shock. Crit Care. 2010;14(4):R126. Available from: 10.1186/cc9091 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 248. Vardakas KZ, Tansarli GS, Rafailidis PI, Falagas ME. Carbapenems versus alternative antibiotics for the treatment of bacteraemia due to Enterobacteriaceae producing extended-spectrum β-lactamases: a systematic review and meta-analysis. J Antimicrob Chemother. 2012;67(12):2793–803. Available from: 10.1093/jac/dks301 [ DOI ] [ PubMed ] [ Google Scholar ] 249. Lee NY, Lee CC, Huang WH, Tsui KC, Hsueh PR, Ko WC. Cefepime therapy for monomicrobial bacteremia caused by cefepime-susceptible extended-spectrum beta-lactamase-producing Enterobacteriaceae: MIC matters. Clin Infect Dis. 2013;56(4):488–95. Available from: 10.1093/cid/cis916 [ DOI ] [ PubMed ] [ Google Scholar ] 250. Dudley MN, Ambrose PG, Bhavnani SM, Craig WA, Ferraro MJ, Jones RN, et al. ; Antimicrobial Susceptibility Testing Subcommittee of the Clinical and Laboratory Standards Institute . Background and rationale for revised Clinical and Laboratory Standards Institute interpretive criteria (breakpoints) for Enterobacteriaceae and Pseudomonas aeruginosa : I. Cephalosporins and aztreonam. Clin Infect Dis. 2013;56(9):1301–9. Available from: 10.1093/cid/cit017 [ DOI ] [ PubMed ] [ Google Scholar ] 251. Harada Y, Morinaga Y, Kaku N, Nakamura S, Uno N, Hasegawa H, et al. In vitro and in vivo activities of piperacillin-tazobactam and meropenem at different inoculum sizes of ESBL-producing Klebsiella pneumoniae . Clin Microbiol Infect. 2014;20(11):O831–9. Available from: 10.1111/1469-0691.12677 [ DOI ] [ PubMed ] [ Google Scholar ] 252. Seo YB, Lee J, Kim YK, Lee SS, Lee JA, Kim HY, et al. Randomized controlled trial of piperacillin-tazobactam, cefepime and ertapenem for the treatment of urinary tract infection caused by extended-spectrum beta-lactamase-producing Escherichia coli . BMC Infect Dis. 2017;17(1):404. Available from: 10.1186/s12879-017-2502-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 253. Munch MW, Granholm A, Jonsson AB, Sjövall F, Helleberg M, Hertz FB, et al. Piperacillin/tazobactam versus carbapenems in patients with severe bacterial infections: a systematic review with meta-analysis. Acta Anaesthesiol Scand. 2023;67(7):853–68. Available from: 10.1111/aas.14239 [ DOI ] [ PubMed ] [ Google Scholar ] 254. Gatti M, Cojutti PG, Pea F. Piperacillin-tazobactam vs. carbapenems for treating hospitalized patients with ESBL-producing Enterobacterales bloodstream infections: a systematic review and meta-analysis. J Glob Antimicrob Resist. 2024;39:27–36. Available from: 10.1016/j.jgar.2024.08.002 [ DOI ] [ PubMed ] [ Google Scholar ] 255. Rac H, Stover KR, Wagner JL, King ST, Warnock HD, Barber KE. Time-kill analysis of ceftolozane/tazobactam efficacy against mucoid Pseudomonas aeruginosa strains from cystic fibrosis patients. Infect Dis Ther. 2017;6(4):507–13. Available from: 10.1007/s40121-017-0176-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 256. Sy SKB, Zhuang L, Xia H, Beaudoin ME, Schuck VJ, Nichols WW, et al. A mathematical model-based analysis of the time-kill kinetics of ceftazidime/avibactam against Pseudomonas aeruginosa. J Antimicrob Chemother. 2018;73(5):1295–304. Available from: 10.1093/jac/dkx537 [ DOI ] [ PubMed ] [ Google Scholar ] 257. Montero MM, Domene Ochoa S, López-Causapé C, Luque S, Sorlí L, Campillo N, et al. Time-kill evaluation of antibiotic combinations containing ceftazidime-avibactam against extensively drug-resistant Pseudomonas aeruginosa and their potential role against ceftazidime-avibactam-resistant isolates. Microbiol Spectr. 2021;9(1):e0058521. Available from: 10.1128/Spectrum.00585-21 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 258. Tam VH, Schilling AN, Nikolaou M. Modelling time-kill studies to discern the pharmacodynamics of meropenem. J Antimicrob Chemother. 2005;55(5):699–706. Available from: 10.1093/jac/dki086 [ DOI ] [ PubMed ] [ Google Scholar ] 259. Cappelletty DM. Evaluation of several dosing regimens of cefepime, with various simulations of renal function, against clinical isolates of Pseudomonas aeruginosa in a pharmacodynamic infection model. Antimicrob Agents Chemother. 1999;43(1):129–33. Available from: 10.1128/AAC.43.1.129 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 260. Burgess DS, Hastings RW. Activity of piperacillin/tazobactam in combination with amikacin, ciprofloxacin, and trovafloxacin against Pseudomonas aeruginosa by time-kill. Diagn Microbiol Infect Dis. 2000;38(1):37–41. Available from: 10.1016/S0732-8893(00)00162-0 [ DOI ] [ PubMed ] [ Google Scholar ] 261. Soares A, Roussel V, Pestel-Caron M, Barreau M, Caron F, Bouffartigues E, et al. Understanding ciprofloxacin failure in Pseudomonas aeruginosa biofilm: persister cells survive matrix disruption. Front Microbiol. 2019;10:2603. Available from: 10.3389/fmicb.2019.02603 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 262. Thomson KS, Moland ES. Cefepime, piperacillin-tazobactam, and the inoculum effect in tests with extended-spectrum beta-lactamase-producing Enterobacteriaceae. Antimicrob Agents Chemother. 2001;45(12):3548–54. Available from: 10.1128/AAC.45.12.3548-3554.2001 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 263. Grégoire N, Raherison S, Grignon C, Comets E, Marliat M, Ploy MC, et al. Semimechanistic pharma-cokinetic-pharmacodynamic model with adaptation development for time-kill experiments of ciprofloxacin against Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2010;54(6):2379–84. Available from: 10.1128/AAC.01478-08 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 264. Hussein M, Barclay J, Baker M, Wu Y, Thombare VJ, Patil N, et al. A comparative review of the pharmacology of dalbavancin and oritavancin for Gram-positive infections: birds of a feather or apples and oranges? Infect Dis Ther. 2025;14(10):2221–46. Available from: 10.1007/s40121-025-01215-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 265. De Alarcón González A. Dalbavancin: a new milestone in the treatment of Gram-positive infections. Enferm Infecc Microbiol Clin. 2025;43(2):61–3. Available from: 10.1016/j.eimc.2024.12.004 [ DOI ] [ PubMed ] [ Google Scholar ] 266. Hidalgo-Tenorio C, Vinuesa D, Plata A, Martin Dávila P, Iftimie S, Sequera S, et al. DALBACEN cohort: dalbavancin as consolidation therapy in patients with endocarditis and/or bloodstream infection produced by Gram-positive cocci. Ann Clin Microbiol Antimicrob. 2019;18(1):30. Available from: 10.1186/s12941-019-0329-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 267. Hidalgo-Tenorio C, Sadyrbaeva-Dolgova S, Enríquez-Gómez A, Muñoz P, Plata-Ciezar A, Miró JM, et al. ; EN-DALBACEN study group . EN-DALBACEN 2.0 Cohort: real-life study of dalbavancin as sequential/consolidation therapy in patients with infective endocarditis due to Gram-positive cocci. Int J Antimicrob Agents. 2023;62(3):106918. Available from: 10.1016/j.ijantimicag.2023.106918 [ DOI ] [ PubMed ] [ Google Scholar ] 268. Van Hise NW, Petrak RM, Shah K, Diaz M, Chundi V, Redell M. Oritavancin versus daptomycin for osteomyelitis treatment after surgical debridement. Infect Dis Ther. 2024;13(3):535–47. Available from: 10.1007/s40121-024-00925-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 269. Raza ZA, Giannini A, Bongiovanni M. Oritavancin for Gram-positive bone and joint infections: a comprehensive review of the literature. Antibiotics (Basel). 2026;15(2):226. Available from: 10.3390/antibiotics15020226 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 270. Moenster RP, Wallace-Lacey A, Western H, Tiefenaur S, Abdulbasir A, Alberts J, et al. Oritavancin vs standard of care for treatment of nonendovascular Gram-positive bloodstream infections. Open Forum Infect Dis. 2023;10(11):ofad411. Available from: 10.1093/ofid/ofad411 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 271. Krsak M, Klimpl D, Mueller SW, Morrisette T, Grimshaw AA, Chastain D, et al. Oritavancin for treatment of osteomyelitis: a systematic review and meta-analysis of observational studies. Pharmacotherapy. 2025;45(11):754–63. Available from: 10.1002/phar.70072 [ DOI ] [ PubMed ] [ Google Scholar ] 272. Biedenbach DJ, Iaconis JP, Sahm DF. Comparative in vitro activities of ceftaroline and ceftriaxone against bacterial pathogens associated with respiratory tract infections: results from the AWARE surveillance study. J Antimicrob Chemother. 2016;71(12):3459–64. Available from: 10.1093/jac/dkw315 [ DOI ] [ PubMed ] [ Google Scholar ] 273. Mootz ML, Britt RS, Mootz AA, Lee GC, Reveles KR, Evoy KE, et al. Comparative-effectiveness of ceftaroline and daptomycin as first-line MRSA therapy for patients with sepsis admitted to hospitals in the United States Veterans Health Care System. Hosp Pract (1995). 2019;47(4):186–91. Available from: 10.1080/21548331.2019.1676540 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 274. Hammond J, Benigno M, Bleibdrey N, Ansari W, Nguyen JL. Ceftaroline fosamil for the treatment of methicillin-resistant Staphylococcus aureus bacteremia: a real-world comparative clinical outcomes study. Drugs Real World Outcomes. 2024;11(2):273–83. Available from: 10.1007/s40801-024-00422-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 275. Torres A, Kuraieva A, Stone GG, Cillóniz C. Systematic review of ceftaroline fosamil in the management of patients with methicillin-resistant Staphylococcus aureus pneumonia. Eur Respir Rev. 2023;32(170):230117. Available from: 10.1183/16000617.0117-2023 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 276. Biedenbach DJ, Iaconis JP, Sahm DF. Comparative in vitro activities of ceftaroline and ceftriaxone against bacterial pathogens associated with respiratory tract infections: results from the AWARE surveillance study. J Antimicrob Chemother. 2016;71(12):3459–64. Available from: 10.1093/jac/dkw315 [ DOI ] [ PubMed ] [ Google Scholar ] 277. Arnés García D, Pitto-Robles I, Calderón Parra J, Calvo Salvador M, Herrero Rodríguez C, Gisbert L, et al. Ceft-to-Ceft Study: real-life experience with ceftaroline and ceftobiprole in treatment of the principal infectious syndromes in a Spanish multicenter hospital cohort. Antibiotics (Basel). 2023;12(12):1692. Available from: 10.3390/antibiotics12121692 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 278. Giacobbe DR, De Rosa FG, Del Bono V, Grossi PA, Pea F, Petrosillo N, et al. Ceftobiprole: drug evaluation and place in therapy. Expert Rev Anti Infect Ther. 2019;17(9):689–98. Available from: 10.1080/14787210.2019.1667229 [ DOI ] [ PubMed ] [ Google Scholar ] 279. Hidalgo-Tenorio C, Membrillo de Novales FJ, Salavert Lleti M, Morata L, Oltra MR, Estella Á, et al. The use of ceftobiprole in treatment pathways in Spain: an expert panel review. J Glob Antimicrob Resist. 2025;43:213–9. Available from: 10.1016/j.jgar.2025.04.021 [ DOI ] [ PubMed ] [ Google Scholar ] 280. Lupia T, Pallotto C, Corcione S, Boglione L, De Rosa FG. Ceftobiprole perspective: current and potential future indications. Antibiotics (Basel). 2021;10(2):170. Available from: 10.3390/antibiotics10020170 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 281. Popejoy MW, Paterson DL, Cloutier D, Huntington JA, Miller B, Bliss CA, et al. Efficacy of ceftolozane/tazobactam against urinary tract and intra-abdominal infections caused by ESBL-producing Escherichia coli and Klebsiella pneumoniae : a pooled analysis of Phase 3 clinical trials. J Antimicrob Chemother. 2017;72(1):268–72. Available from: 10.1093/jac/dkw374 [ DOI ] [ PubMed ] [ Google Scholar ] 282. Kollef MH, Timsit JF, Martin-Loeches I, Wunderink RG, Huntington JA, Jensen EH, et al. Outcomes in participants with failure of initial antibacterial therapy for hospital-acquired/ventilator-associated bacterial pneumonia prior to enrollment in the randomized, controlled phase 3 ASPECT-NP trial of ceftolozane/tazobactam versus meropenem. Crit Care. 2022;26(1):373. Available from: 10.1186/s13054-022-04192-w [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 283. Del Barrio-Tofiño E, Zamorano L, Cortes-Lara S, López-Causapé C, Sánchez-Diener I, Cabot G, et al. ; GEMARA-SEIMC/REIPI Pseudomonas study Group . Spanish nationwide survey on Pseudomonas aeruginosa antimicrobial resistance mechanisms and epidemiology. J Antimicrob Chemother. 2019;74(7):1825–35. Available from: 10.1093/jac/dkz147 [ DOI ] [ PubMed ] [ Google Scholar ] 284. Jorgensen SCJ, Trinh TD, Zasowski EJ, Lagnf AM, Simon SP, Bhatia S, et al. Real-world experience with ceftolozane-tazobactam for multidrug-resistant Gram-negative bacterial infections. Antimicrob Agents Chemother. 2020;64(4):e02291–19. Available from: 10.1128/AAC.02291-19 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 285. Gallagher JC, Satlin MJ, Elabor A, Saraiya N, McCreary EK, Molnar E, et al. Ceftolozane-tazobactam for the treatment of multidrug-resistant Pseudomonas aeruginosa infections: a multicenter study. Open Forum Infect Dis. 2018;5(11):ofy280. Available from: 10.1093/ofid/ofy280 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 286. Stone GG, Newell P, Gasink LB, Broadhurst H, Wardman A, Yates K, et al. Clinical activity of ceftazidime/avibactam against MDR Enterobacteriaceae and Pseudomonas aeruginosa : pooled data from the ceftazidime/avibactam Phase III clinical trial programme. J Antimicrob Chemother. 2018;73(9):2519–23. Available from: 10.1093/jac/dky204 [ DOI ] [ PubMed ] [ Google Scholar ] 287. Soriano A, Montravers P, Bassetti M, Klyasova G, Daikos G, Irani P, et al. The use and effectiveness of ceftazidime-avibactam in real-world clinical practice: EZTEAM Study. Infect Dis Ther. 2023;12(3):891–917. Available from: 10.1007/s40121-023-00762-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 288. Bassetti M, Carannante N, Pallotto C, Righi E, Di Caprio G, Bernardo M, et al. KPC-producing Klebsiella pneumoniae gut decolonisation following ceftazidime/avibactam-based combination therapy: a retrospective observational study. J Glob Antimicrob Resist. 2019;17:109–11. Available from: 10.1016/j.jgar.2018.11.014 [ DOI ] [ PubMed ] [ Google Scholar ] 289. van Duin D, Lok JJ, Earley M, Cober E, Richter SS, Perez F, et al. ; Antibacterial Resistance Leadership Group . Colistin versus ceftazidime-avibactam in the treatment of infections due to carbapenem-resistant Enterobacteriaceae. Clin Infect Dis. 2018;66(2):163–71. Available from: 10.1093/cid/cix783 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 290. Castón JJ, Lacort-Peralta I, Martín-Dávila P, Loeches B, Tabares S, Temkin L, et al. Clinical efficacy of ceftazidime/avibactam versus other active agents for the treatment of bacteremia due to carbapenemase-producing Enterobacteriaceae in hematologic patients. Int J Infect Dis. 2017;59:118–23. Available from: 10.1016/j.ijid.2017.03.021 [ DOI ] [ PubMed ] [ Google Scholar ] 291. Matesanz M, Mensa J. Ceftazidime-avibactam. Rev Esp Quimioter. 2021;34 Suppl 1:38–40. Available from: 10.37201/req/s01.11.2021 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 292. Wagenlehner F, Caballero VR, Maheshwari V, Biswas A, Saini P, Quevedo J, et al. Efficacy of treatment options for complicated urinary tract infections including acute pyelonephritis: a systematic literature review and network meta-analysis. J Comp Eff Res. 2025;14(3):e240214. Available from: 10.57264/cer-2024-0214 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 293. Bassetti M, Giacobbe DR, Patel N, Tillotson G, Massey J. Efficacy and safety of meropenem-vaborbactam versus best available therapy for the treatment of carbapenem-resistant Enterobacteriaceae infections in patients without prior antimicrobial failure: a post hoc analysis. Adv Ther. 2019;36(7):1771–7. Available from: 10.1007/s12325-019-00981-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 294. Novelli A, Del Giacomo P, Rossolini GM, Tumbarello M. Meropenem/vaborbactam: a next generation β-lactam β-lactamase inhibitor combination. Expert Rev Anti Infect Ther. 2020;18(7):643–55. Available from: 10.1080/14787210.2020.1756775 [ DOI ] [ PubMed ] [ Google Scholar ] 295. Jin D, Hu D, Jin Y. Real-world effectiveness and safety of meropenem-vaborbactam in the treatment of carbapenem-resistant Enterobacterales (CRE) infections: a systematic review and meta-analysis. J Chemother. 2025;37(8):653–61. Available from: 10.1080/1120009X.2025.2465129 [ DOI ] [ PubMed ] [ Google Scholar ] 296. Zilberberg MD, Nathanson BH, Redell MA, Sulham K, Shorr AF. Comparative outcomes of meropenem-vaborbactam vs. ceftazidime-avibactam among adults hospitalized with an infectious syndrome in the US, 2019-2021. Antibiotics (Basel). 2025;14(1):29. Available from: 10.3390/antibiotics14010029 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 297. Bassetti M, Giacobbe DR, Vena A, Poulakou G, Rossolini GM, Soriano A, et al. Meropenem-vaborbactam for treatment of carbapenem-resistant Enterobacterales: a narrative review of clinical practice evidence. Infect Dis Ther. 2025;14(5):973–89. Available from: 10.1007/s40121-025-01146-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 298. Mohammad S, Al Namer Y, Rahimeh W, Albalas M, Almangour TA. Effectiveness and safety of meropenem-vaborbactam versus ceftazidime-avibactam in multidrug-resistant Gram-negative infections: a systematic review and meta-analysis with trial sequential analysis. Antimicrob Agents Chemother. 2026;70(2):e0154625. Available from: 10.1128/aac.01546-25 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 299. Portsmouth S, van Veenhuyzen D, Echols R, Machida M, Ferreira JCA, Ariyasu M, et al. Cefiderocol versus imipenem-cilastatin for the treatment of complicated urinary tract infections caused by Gram-negative uropathogens: a phase 2, randomised, double-blind, non-inferiority trial. Lancet Infect Dis. 2018;18(12):1319–28. Available from: 10.1016/S1473-3099(18)30554-1 [ DOI ] [ PubMed ] [ Google Scholar ] 300. Risco-Risco C, Henriquez-Camacho C, Herrera-Rueda M, Barberán J, Andaluz-Ojeda D. Cefiderocol versus best available therapy in the treatment of critically ill patients with severe infections due to resistant Gram-negative bacteria: a systematic review and meta-analysis. Antibiotics (Basel). 2024;13(11):1048. Available from: 10.3390/antibiotics13111048 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 301. Bleibtreu A, Bennis Y, Dinh A, Dupont H, Friggeri A, Jeannot K, et al. ; CoRIS Cohort . Role of cefiderocol in the treatment of carbapenem-resistant nonfermenting Gram-negative bacilli ( Acinetobacter baumannii, Pseudomonas aeruginosa , and Stenotrophomonas maltophilia ) infections: an expert statement. Infect Dis Ther. 2026;15(3):835–57. Available from: 10.1007/s40121-026-01301-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 302. Torre-Cisneros J, Almirante B, Martos CF, Rascado P, Lletí MS, Sánchez-García M, et al. Effectiveness and safety of cefiderocol treatment in patients with Gram-negative bacterial infections in Spain in the early access programme: results of the PERSEUS study. Eur J Clin Microbiol Infect Dis. 2025;44(6):1375–90. Available from: 10.1007/s10096-025-05108-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 303. Torre-Cisneros J, Ferrer R, Martos CF, Sarda J, Gonzalez Calvo AJ, Verardi S, et al. Cefiderocol treatment for patients infected by Stenotrophomonas maltophilia, Burkholderia cepacia complex and Achromobacter spp.: subgroup analysis from the PERSEUS study. Eur J Clin Microbiol Infect Dis. 2025;44(6):1367–74. Available from: 10.1007/s10096-025-05109-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 304. Russo A, Bruni A, Gullì S, Borrazzo C, Quirino A, Lionello R, et al. Efficacy of cefiderocol- vs colistin-containing regimen for treatment of bacteraemic ventilator-associated pneumonia caused by carbapenem-resistant Acinetobacter baumannii in patients with COVID-19. Int J Antimicrob Agents. 2023;62(1):106825. Available from: 10.1016/j.ijantimicag.2023.106825 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 305. Dalfino L, Stufano M, Bavaro DF, Diella L, Belati A, Stolfa S, et al. Effectiveness of first-line therapy with old and novel antibiotics in ventilator-associated pneumonia caused by carbapenem-resistant Acinetobacter baumannii : a real life, prospective, observational, single-center study. Antibiotics (Basel). 2023;12(6):1048. Available from: 10.3390/antibiotics12061048 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 306. Zhan Y, Mao W, Zhao C, Lu D, Chen C, Hu W, et al. Comparison of cefiderocol and colistin-based regimens for the treatment of severe infections caused by carbapenem-resistant Acinetobacter baumannii : a systematic review with meta-analysis and trial sequential analysis. BMC Infect Dis. 2024;24(1):967. Available from: 10.1186/s12879-024-09899-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 307. Viale P, Sandrock CE, Ramirez P, Rossolini GM, Lodise TP. Treatment of critically ill patients with cefiderocol for infections caused by multidrug-resistant pathogens: review of the evidence. Ann Intensive Care. 2023;13(1):52. Available from: 10.1186/s13613-023-01146-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 308. Giannella M, Verardi S, Karas A, Abdel Hadi H, Dupont H, Soriano A, et al. ; ARES Study Group . Carbapenem-resistant Acinetobacter spp. infection in critically ill patients with limited treatment options: a descriptive study of cefiderocol therapy during the COVID-19 pandemic. Open Forum Infect Dis. 2023;10(7):ofad329. Available from: 10.1093/ofid/ofad329 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 309. Marcella S, Friedman B, Cai B, Verardi S, Gozalo L, Nguyen ST. Outcomes of patients with Stenotrophomonas maltophilia infections treated with cefiderocol in PROVE (Retrospective Cefiderocol Chart Review) Study. Open Forum Infect Dis. 2023;10 Suppl 2:ofad500.2363. Available from: 10.1093/ofid/ofad500.2363 [ DOI ] [ Google Scholar ] 310. Larcher R, Pearson J, Verardi S, Karas A, Santerre-Henriksen A, Pecini R. Real-world use of cefiderocol in the EU and US for Pseudomonas aeruginosa : interim data from the PROVE study. 33rd European Congress of Clinical Microbiology and Infectious Diseases (ECCMID); 2023. [ Google Scholar ] 311. Kimbrough JH, Karr MH, Nguyen ST, DeJonge BLM, Longshaw C, Takemura M, et al. Activity of cefiderocol against Pseudomonas aeruginosa from the USA and Europe (2020-2023) with difficult-to-treat resistance phenotype, including those nonsusceptible to recently developed β-lactam/β-lactamase inhibitor combinations: results from the SENTRY antimicrobial surveillance program. Microbiol Spectr. 2025;13(11):e0207925. Available from: 10.1128/spectrum.02079-25 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 312. Timsit JF, Durante-Mangoni E, Sanders J, Verardi S, Karas A, Henriksen A. Real-world use of cefiderocol in the EU and US for Acinetobacter baumannii : interim data from the PROVE study. 33rd European Congress of Clinical Microbiology and Infectious Diseases (ECCMID); 2023. [ Google Scholar ] 313. Patauner F, Durante-Mangoni E. Cefiderocol for resistant Gram negatives: linking in vitro activity with clinical effectiveness. Clin Microbiol Infect. 2024;30(2):155–8. Available from: 10.1016/j.cmi.2023.11.012 [ DOI ] [ PubMed ] [ Google Scholar ] 314. Lei W, Duan Y, Xin M, Tian M, Xu J. Efficacy and safety of imipenem/cilastatin/relebactam (IMI/CS/REL): a meta-analysis of randomized controlled clinical trials. BMC Infect Dis. 2025;25(1):1149. Available from: 10.1186/s12879-025-11499-w [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 315. Shields RK, Martin-Loeches I, Yücel E, Bagga S, Pal Kaur Khurana V, Soni P, et al. Exploring the effectiveness of imipenem/relebactam in patients with antimicrobial-resistant hospital-acquired infections: findings from systematic literature reviews. Antibiotics (Basel). 2026;15(2):170. Available from: 10.3390/antibiotics15020170 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 316. Caniff KE, Rebold N, Xhemali X, Tran N, Eubank TA, Garey KW, et al. Real-world applications of imipenem-cilastatin-relebactam: insights from a multicenter observational cohort study. Open Forum Infect Dis. 2025;12(4):ofaf112. Available from: 10.1093/ofid/ofaf112 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 317. Jorgensen SCJ, Mercuro NJ, Davis SL, Rybak MJ. Delafloxacin: place in therapy and review of microbiologic, clinical and pharmacologic properties. Infect Dis Ther. 2018;7(2):197–217. Available from: 10.1007/s40121-018-0198-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 318. Sharma R, Sandrock CE, Meehan J, Theriault N. Community-acquired bacterial pneumonia: changing epidemiology, resistance patterns, and newer antibiotics: spotlight on delafloxacin. Clin Drug Investig. 2020;40(10):947–60. Available from: 10.1007/s40261-020-00953-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 319. Vlachaki I, Vacchelli M, Zinzi D, Falla E, Jiang Y, Mantopoulos T, et al. Comparative efficacy of delafloxacin for complicated and acute bacterial skin and skin structure infections: results from a network meta-analysis. BMC Infect Dis. 2021;21(1):1036. Available from: 10.1186/s12879-021-06736-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 320. Craddock VD, Steere EL, Harman H, Britt NS. Activity of delafloxacin and comparator fluoroquinolones against multidrug-resistant Pseudomonas aeruginosa in an in vitro cystic fibrosis sputum model. Antibiotics (Basel). 2023;12(6):1078. Available from: 10.3390/antibiotics12061078 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 321. Ribeiro ÁCDS, Santos FF, Valiatti TB, Lenzi MH, Santos INMNeves RFB, et al. Comparative in vitro activity of delafloxacin and other antimicrobials against isolates from patients with acute bacterial skin, skin-structure infection and osteomyelitis. Braz J Infect Dis. 2024;28(6):103867. Available from: 10.1016/j.bjid.2024.103867 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 322. Cercenado E, Murillo O. Delafloxacin: what does a new fluoroquinolone offer? Rev Esp Quimioter. 2026;39(2):104–24. Available from: 10.37201/req/151.2026 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 323. Kaye KS, Belley A, Barth P, Lahlou O, Knechtle P, Motta P, et al. Effect of cefepime/enmetazobactam vs piperacillin/tazobactam on clinical cure and microbiological eradication in patients with complicated urinary tract infection or acute pyelonephritis: a randomized clinical trial. JAMA. 2022;328(13):1304–14. Available from: 10.1001/jama.2022.17034 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 324. Darlow CA, Hope W, Dubey V. Cefepime/enmetazobactam: a microbiological, pharmacokinetic, pharmacodynamic, and clinical evaluation. Expert Opin Drug Metab Toxicol. 2025;21(2):115–23. Available from: 10.1080/17425255.2024.2427310 [ DOI ] [ PubMed ] [ Google Scholar ] 325. Ward J, Dunkel N, Quevedo J, Morrisey I, Santerre Henriksen A. In vitro activity of cefepime/enmetazobactam, and comparators, against OXA carbapenemase-positive Acinetobacter baumannii . JAC Antimicrob Resist. 2025;7 Suppl 1:dlae217.028. Available from: 10.1093/jacamr/dlae217.028 [ DOI ] [ Google Scholar ] 326. Wagenlehner FME, Cloutier DJ, Komirenko AS, Cebrik DS, Krause KM, Keepers TR, et al. ; EPIC Study Group . Once-daily plazomicin for complicated urinary tract infections. N Engl J Med. 2019;380(8):729–40. Available from: 10.1056/NEJMoa1801467 [ DOI ] [ PubMed ] [ Google Scholar ] 327. Meng R, Guan X, Sun L, Fei Z, Li Y, Luo M, et al. The efficacy and safety of eravacycline compared with current clinically common antibiotics in the treatment of adults with complicated intra-abdominal infections: a Bayesian network meta-analysis. Front Med (Lausanne). 2022;9:935343. Available from: 10.3389/fmed.2022.935343 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 328. Huang PY, Hsu CK, Tang HJ, Lai CC. Eravacycline: a comprehensive review of in vitro activity, clinical efficacy, and real-world applications. Expert Rev Anti Infect Ther. 2024;22(6):387–98. Available from: 10.1080/14787210.2024.2351552 [ DOI ] [ PubMed ] [ Google Scholar ] 329. Kunz Coyne AJ, Alosaimy S, Lucas K, Lagnf AM, Morrisette T, Molina KC, et al. Eravacycline, the first four years: health outcomes and tolerability data for 19 hospitals in 5 U.S. regions from 2018 to 2022. Microbiol Spectr. 2024;12(1):e0235123. Available from: 10.1128/spectrum.02351-23 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 330. Falagas ME, Romanos LT, Kontogiannis DS, Filippou C, Karageorgopoulos DE. Resistance of Gram-negative bacteria to eravacycline: a systematic review of data from in vitro studies. Pathogens. 2025;14(12):1214. Available from: 10.3390/pathogens14121214 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 331. Albanell-Fernández M, Verdejo MÁ, Barberán J, Mensa J, Soriano Á. Eravacycline: from broad-spectrum coverage to novel clinical applications. Rev Esp Quimioter. 2026;39(2):89–103. Available from: 10.37201/req/141.2025 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 332. Smith A, White R. Comparative Monte-Carlo analysis of aztreonam-avibactam vs. ceftazidime-avibactam against carbapenem-resistant Gram-negative pathogens. Open Forum Infect Dis. 2018;5 Suppl 1:S439. Available from: 10.1093/ofid/ofy210.1253 [ DOI ] [ Google Scholar ] 333. Emeraud C, Dutkiewicz M, Dortet L. Comparative in vitro efficacy of aztreonam-avibactam and other alternatives (cefepime-taniborbactam, cefepime-zidebactam, and cefiderocol) against metallo-β-lactamase-producing Enterobacterales isolated in 2024 in France. Antimicrob Agents Chemother. 2026:e0017626. Available from: 10.1128/aac.00176-26 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 334. Al Musawa M, Bleick CR, Herbin SR, Caniff KE, Van Helden SR, Rybak MJ. Aztreonam-avibactam: the dynamic duo against multidrug-resistant Gram-negative pathogens. Pharmacotherapy. 2024;44(12):927–38. Available from: 10.1002/phar.4629 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 335. Giuliano S, Moreal C, Angelini J, Vila A, Bonomo RA, Tamma PD, et al. A comparative evaluation of aztreonam-avibactam and ceftazidime-avibactam plus aztreonam for infections due to MBL-producing Enterobacterales. Int J Infect Dis. 2026:108716. Available from: 10.1016/j.ijid.2026.108716 [ DOI ] [ PubMed ] [ Google Scholar ] 336. Chen C, Mao W, Zhao C, Lu D, Hu W, Xiong J. Comparative effectiveness and safety of aztreonam-avibactam or ceftazidime-avibactam plus aztreonam versus best available therapy for severe Gram-negative infections: a systematic review, meta-analysis, and trial sequential analysis. Eur J Clin Microbiol Infect Dis. 2026. Available from: 10.1007/s10096-026-05482-9 [ DOI ] [ PubMed ] [ Google Scholar ] 337. Garcia-Effron G. Rezafungin: mechanisms of action, susceptibility and resistance: similarities and differences with the other echinocandins. J Fungi (Basel). 2020;6(4):262. Available from: 10.3390/jof6040262 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 338. Soriano A, Honore PM, Cornely OA, Chayakulkeeree M, Bassetti M, Haihui H, et al. Treatment outcomes among patients with a positive Candida culture close to randomization receiving rezafungin or caspofungin in the ReSTORE Study. Clin Infect Dis. 2024;79(3):672–81. Available from: 10.1093/cid/ciae363 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 339. Losito AR, Raffaelli F, Del Giacomo P, Tumbarello M. New drugs for the treatment of Pseudomonas aeruginosa infections with limited treatment options: a narrative review. Antibiotics (Basel). 2022;11(5):579. Available from: 10.3390/antibiotics11050579 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 340. Barbier F, Hraiech S, Kernéis S, Veluppillai N, Pajot O, Poissy J, et al. ; French Intensive Care Society . Rationale and evidence for the use of new beta-lactam/beta-lactamase inhibitor combinations and cefiderocol in critically ill patients. Ann Intensive Care. 2023;13(1):65. Available from: 10.1186/s13613-023-01153-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 341. Yahav D, Giske CG, Grāmatniece A, Abodakpi H, Tam VH, Leibovici L. New β-lactam-β-lactamase inhibitor combinations. Clin Microbiol Rev. 2020;34(1):e00115–20. Available from: 10.1128/CMR.00115-20 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 342. Mladenović-Antić S, Veličković-Radovanović R, Stojanović P, Randjelović M, Djordjević V. In vitro activity of ceftazidime-avibactam, ceftolozane-tazobactam, imipenem-relebactam, meropenem-vaborbactam and cefiderocol against carbapenemase-producing Enterobacterales from clinical isolates in a tertiary healthcare centre in Serbia. Acta Microbiol Immunol Hung. 2025;72(1):23–32. Available from: 10.1556/030.2025.02521 [ DOI ] [ PubMed ] [ Google Scholar ] 343. Valero A, Rodríguez-Gascón A, Isla A, Barrasa H, Del Barrio-Tofiño E, Oliver A, et al. Pseudomonas aeruginosa susceptibility in Spain: antimicrobial activity and resistance suppression evaluation by PK/PD analysis. Pharmaceutics. 2021;13(11):1899. Available from: 10.3390/pharmaceutics13111899 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 344. Lombardi A, Alagna L, Palomba E, Viero G, Tonizzo A, Mangioni D, et al. New antibiotics against multidrug-resistant Gram-negative bacteria in liver transplantation: clinical perspectives, toxicity, and PK/PD properties. Transpl Int. 2024;37:11692. Available from: 10.3389/ti.2024.11692 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 345. Bavaro DF, Accurso G, Corcione S, Vena A, Schenone M, Diella L, et al. Antipseudomonal cephalosporins versus piperacillin/tazobactam or carbapenems for the definitive antibiotic treatment of Pseudomonas aeruginosa bacteraemia: new kids on the ICU block? J Antimicrob Chemother. 2025;80(5):1342–53. Available from: 10.1093/jac/dkaf080 [ DOI ] [ PubMed ] [ Google Scholar ] 346. Li J, Zhao M, He Q. Association of antibiotic type and timing with sepsis mortality using target trial emulation. Sci Rep. 2026;16:10447. Available from: 10.1038/s41598-026-40860-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 347. Rodríguez-Baño J, Gutiérrez-Gutiérrez B, Machuca I, Pascual A. Treatment of infections caused by extended-spectrum-beta-lactamase-, AmpC-, and carbapenemase-producing Enterobacteriaceae. Clin Microbiol Rev. 2018;31(2):e00079–17. Available from: 10.1128/CMR.00079-17 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 348. Karaiskos I, Giamarellou H. Carbapenem-sparing strategies for ESBL producers: when and how. Antibiotics (Basel). 2020;9(2):61. Available from: 10.3390/antibiotics9020061 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 349. Díaz Santos E, Mora Jiménez C, Del Río-Carbajo L, Vidal-Cortés P. Treatment of severe multi-drug resistant Pseudomonas aeruginosa infections. Med Intensiva (Engl Ed). 2022;46(9):508–20. Available from: 10.1016/j.medine.2022.06.014 [ DOI ] [ PubMed ] [ Google Scholar ] 350. Pina-Sánchez M, Rua M, Del Pozo JL. Present and future of resistance in Pseudomonas aeruginosa : implications for treatment. Rev Esp Quimioter. 2023;36 Suppl 1:54–8. Available from: 10.37201/req/s01.13.2023 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 351. Hidalgo-Tenorio C, Bou G, Oliver A, Rodríguez-Aguirregabiria M, Salavert M, Martínez-Martínez L. The challenge of treating infections caused by metallo-β-lactamase-producing Gram-negative bacteria: a narrative review. Drugs. 2024;84(12):1519–39. Available from: 10.1007/s40265-024-02102-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 352. Xu W, Shang L, Li X, Yu J, Shen J, Li Y, et al. Current progress in antibacterial agents for carbapenem-resistant Enterobacterales. Front Pharmacol. 2026;17:1796557. Available from: 10.3389/fphar.2026.1796557 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 353. Karvouniaris M, Almyroudi MP, Abdul-Aziz MH, Blot S, Paramythiotou E, Tsigou E, et al. Novel antimicrobial agents for Gram-negative pathogens. Antibiotics (Basel). 2023;12(4):761. Available from: 10.3390/antibiotics12040761 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 354. Drusano GL, Louie A, MacGowan A, Hope W. Suppression of emergence of resistance in pathogenic bacteria: keeping our powder dry, part 1. Antimicrob Agents Chemother. 2016;60(3):1183–93. Available from: 10.1128/AAC.02177-15 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 355. Drusano GL, Hope W, MacGowan A, Louie A. Suppression of emergence of resistance in pathogenic bacteria: keeping our powder dry, part 2. Antimicrob Agents Chemother. 2016;60(3):1194–201. Available from: 10.1128/AAC.02231-15 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 356. Howatt M, Klompas M, Kalil AC, Metersky ML, Muscedere J. Carbapenem antibiotics for the empiric treatment of nosocomial pneumonia: a systematic review and meta-analysis. Chest. 2021;159(3):1041–54. Available from: 10.1016/j.chest.2020.10.039 [ DOI ] [ PubMed ] [ Google Scholar ] 357. Zanetti G, Bally F, Greub G, Garbino J, Kinge T, Lew D, et al. ; Cefepime Study Group . Cefepime versus imipenem-cilastatin for treatment of nosocomial pneumonia in intensive care unit patients: a multicenter, evaluator-blind, prospective, randomized study. Antimicrob Agents Chemother. 2003;47(11):3442–7. Available from: 10.1128/AAC.47.11.3442-3447.2003 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 358. Kiang TK, Häfeli UO, Ensom MH. A comprehensive review on the pharmacokinetics of antibiotics in interstitial fluid spaces in humans: implications on dosing and clinical pharmacokinetic monitoring. Clin Pharmacokinet. 2014;53(8):695–730. Available from: 10.1007/s40262-014-0152-3 [ DOI ] [ PubMed ] [ Google Scholar ] 359. Lee DG, Murakami Y, Andes DR, Craig WA. Inoculum effects of ceftobiprole, daptomycin, linezolid, and vancomycin with Staphylococcus aureus and Streptococcus pneumoniae at inocula of 10(5) and 10(7) CFU injected into opposite thighs of neutropenic mice. Antimicrob Agents Chemother. 2013;57(3):1434–41. Available from: 10.1128/AAC.00362-12 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 360. Jacqueline C, Caillon J. Impact of bacterial biofilm on the treatment of prosthetic joint infections. J Antimicrob Chemother. 2014;69 Suppl 1:i37–40. Available from: 10.1093/jac/dku254 [ DOI ] [ PubMed ] [ Google Scholar ] 361. Frei CR, Wiederhold NP, Burgess DS. Antimicrobial breakpoints for Gram-negative aerobic bacteria based on pharmacokinetic-pharmacodynamic models with Monte Carlo simulation. J Antimicrob Chemother. 2008;61(3):621–8. Available from: 10.1093/jac/dkm536 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 362. Drusano GL. Pharmacokinetics and pharmacodynamics of antimicrobials. Clin Infect Dis. 2007;45 Suppl 1:S89–95. Available from: 10.1086/518137 [ DOI ] [ PubMed ] [ Google Scholar ] 363. Rodríguez-Núñez O, Periañez-Parraga L, Oliver A, Munita JM, Boté A, Gasch O, et al. Higher MICs (>2 mg/L) predict 30-day mortality in patients with lower respiratory tract infections caused by multidrug- and extensively drug-resistant Pseudomonas aeruginosa treated with ceftolozane/tazobactam. Open Forum Infect Dis. 2019;6(10):ofz416. Available from: 10.1093/ofid/ofz416 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 364. Dulhunty JM, Roberts JA, Davis JS, Webb SA, Bellomo R, Gomersall C, et al. Continuous infusion of beta-lactam antibiotics in severe sepsis: a multicenter double-blind, randomized controlled trial. Clin Infect Dis. 2013;56(2):236–44. Available from: 10.1093/cid/cis856 [ DOI ] [ PubMed ] [ Google Scholar ] 365. Pintado V, Ruiz-Garbajosa P, Aguilera-Alonso D, Baquero-Artigao F, Bou G, Cantón R, et al. Executive summary of the consensus document of the Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC) on the diagnosis and antimicrobial treatment of infections due to carbapenem-resistant Gram-negative bacteria. Enferm Infecc Microbiol Clin (Engl Ed). 2023;41(6):360–70. Available from: 10.1016/j.eimce.2022.06.014 [ DOI ] [ PubMed ] [ Google Scholar ] 366. Fawaz S, Barton S, Nabhani-Gebara S. Comparing clinical outcomes of piperacillin-tazobactam administration and dosage strategies in critically ill adult patients: a systematic review and meta-analysis. BMC Infect Dis. 2020;20(1):430. Available from: 10.1186/s12879-020-05149-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 367. Rhodes NJ, Liu J, O’Donnell JN, Dulhunty JM, Abdul-Aziz MH, Berko PY, et al. Prolonged infusion piperacillin-tazobactam decreases mortality and improves outcomes in severely ill patients: results of a systematic review and meta-analysis. Crit Care Med. 2018;46(2):236–43. Available from: 10.1097/CCM.0000000000002836 [ DOI ] [ PubMed ] [ Google Scholar ] 368. Maduranga S, Valencia BM, Li X, Moallemi S, Rodrigo C. A systematic review and meta-analysis of comparative clinical studies on antibiotic treatment of brucellosis. Sci Rep. 2024;14(1):19037. Available from: 10.1038/s41598-024-69669-w [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 369. Nasomsong W, Nulsopapon P, Changpradub D, Pungcharoenkijkul S, Hanyanunt P, Chatreewattanakul T, et al. Optimizing doses of ceftolozane/tazobactam as monotherapy or in combination with amikacin to treat carbapenem-resistant Pseudomonas aeruginosa . Antibiotics (Basel). 2022;11(4):517. Available from: 10.3390/antibiotics11040517 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 370. Lipman J, Lewis RE. The long walk to a short half-life: the discovery of augmented renal clearance and its impact on antibiotic dosing. J Antimicrob Chemother. 2025;80(12):3367–74. Available from: 10.1093/jac/dkaf378 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 371. Li L, Li X, Xia Y, Chu Y, Zhong H, Li J, et al. Recommendation of antimicrobial dosing optimization during continuous renal replacement therapy. Front Pharmacol. 2020;11:786. Available from: 10.3389/fphar.2020.00786 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 372. Monogue ML, Sanders JM, Mercuro NJ, Hodge CK, Golnabi E, Carettini T, et al. Antimicrobial pharmacokinetic and pharmacodynamic considerations in special populations: a call to action. Open Forum Infect Dis. 2026;13(3):ofag093. Available from: 10.1093/ofid/ofag093 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 373. Zarkotou O, Pournaras S, Tselioti P, Dragoumanos V, Pitiriga V, Ranellou K, et al. Predictors of mortality in patients with bloodstream infections caused by KPC-producing Klebsiella pneumoniae and impact of appropriate antimicrobial treatment. Clin Microbiol Infect. 2011;17(12):1798–803. Available from: 10.1111/j.1469-0691.2011.03514.x [ DOI ] [ PubMed ] [ Google Scholar ] 374. Tumbarello M, Viale P, Viscoli C, Trecarichi EM, Tumietto F, Marchese A, et al. Predictors of mortality in bloodstream infections caused by Klebsiella pneumoniae carbapenemase-producing K. pneumoniae : importance of combination therapy. Clin Infect Dis. 2012;55(7):943–50. Available from: 10.1093/cid/cis588 [ DOI ] [ PubMed ] [ Google Scholar ] 375. Drusano GL, Liu W, Fregeau C, Kulawy R, Louie A. Differing effects of combination chemotherapy with meropenem and tobramycin on cell kill and suppression of resistance of wild-type Pseudomonas aeruginosa PAO1 and its isogenic MexAB efflux pump-overexpressed mutant. Antimicrob Agents Chemother. 2009;53(6):2266–73. Available from: 10.1128/AAC.01680-08 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 376. Tamma PD, Cosgrove SE, Maragakis LL. Combination therapy for treatment of infections with Gram-negative bacteria. Clin Microbiol Rev. 2012;25(3):450–70. Available from: 10.1128/CMR.05041-11 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 377. Tängdén T. Combination antibiotic therapy for multidrug-resistant Gram-negative bacteria. Ups J Med Sci. 2014;119(2):149–53. Available from: 10.3109/03009734.2014.899279 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 378. Micek ST, Welch EC, Khan J, Pervez M, Doherty JA, Reichley RM, et al. Empiric combination antibiotic therapy is associated with improved outcome against sepsis due to Gram-negative bacteria: a retrospective analysis. Antimicrob Agents Chemother. 2010;54(5):1742–8. Available from: 10.1128/AAC.01365-09 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 379. Montero M, VanScoy BD, López-Causapé C, Conde H, Adams J, Segura C, et al. Evaluation of ceftolozane-tazobactam in combination with meropenem against Pseudomonas aeruginosa sequence type 175 in a hollow-fiber infection model. Antimicrob Agents Chemother. 2018;62(5):e00026–18. Available from: 10.1128/AAC.00026-18 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 380. Martin A, Fahrbach K, Zhao Q, Lodise T. Association between carbapenem resistance and mortality among adult, hospitalized patients with serious infections due to Enterobacteriaceae: results of a systematic literature review and meta-analysis. Open Forum Infect Dis. 2018;5(7):ofy150. Available from: 10.1093/ofid/ofy150 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 381. Schmid A, Wolfensberger A, Nemeth J, Schreiber PW, Sax H, Kuster SP. Monotherapy versus combination therapy for multidrug-resistant Gram-negative infections: systematic review and meta-analysis. Sci Rep. 2019;9(1):15290. Available from: 10.1038/s41598-019-51711-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 382. Olsson A, Wistrand-Yuen P, Nielsen EI, Friberg LE, Sandegren L, Lagerbäck P, et al. Efficacy of antibiotic combinations against multidrug-resistant Pseudomonas aeruginosa in automated time-lapse microscopy and static time-kill experiments. Antimicrob Agents Chemother. 2020;64(6):e02111–19. Available from: 10.1128/AAC.02111-19 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 383. Umemura T, Kato H, Hagihara M, Hirai J, Yamagishi Y, Mikamo H. Efficacy of combination therapies for the treatment of multi-drug resistant Gram-negative bacterial infections based on meta-analyses. Antibiotics (Basel). 2022;11(4):524. Available from: 10.3390/antibiotics11040524 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 384. Nath S, Moussavi F, Abraham D, Landman D, Quale J. In vitro and in vivo activity of single and dual antimicrobial agents against KPC-producing Klebsiella pneumoniae . J Antimicrob Chemother. 2018;73(2):431–6. Available from: 10.1093/jac/dkx419 [ DOI ] [ PubMed ] [ Google Scholar ] 385. Mikhail S, Singh NB, Kebriaei R, Rice SA, Stamper KC, Castanheira M, et al. Evaluation of the synergy of ceftazidime-avibactam in combination with meropenem, amikacin, aztreonam, colistin, or fosfomycin against well-characterized multidrug-resistant Klebsiella pneumoniae and Pseudomonas aeruginosa . Antimicrob Agents Chemother. 2019;63(8):e00779–19. Available from: 10.1128/AAC.00779-19 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 386. Kang Y, Cui J. Pharmacodynamic target attainment of the synergism of ceftazidime-avibactam in combination with amikacin against OXA-producing extensively drug-resistant or pan drug-resistant (XDR/PDR) Pseudomonas aeruginosa . Eur J Clin Microbiol Infect Dis. 2025;44(6):1493–500. Available from: 10.1007/s10096-025-05090-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 387. Jones F, Hu Y, Coates A. The efficacy of using combination therapy against multi-drug and extensively drug-resistant Pseudomonas aeruginosa in clinical settings. Antibiotics (Basel). 2022;11(3):323. Available from: 10.3390/antibiotics11030323 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 388. Gill CM, Santini D, Takemura M, Longshaw C, Yamano Y, Echols R, et al. In vivo efficacy and resistance prevention of cefiderocol in combination with ceftazidime/avibactam, ampicillin/sulbactam or meropenem using human-simulated regimens versus Acinetobacter baumannii . J Antimicrob Chemother. 2023;78(4):983–90. Available from: 10.1093/jac/dkad032 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 389. Sakoulas G. “Outside the box” drug combinations for multidrug-resistant Acinetobacter baumannii . NEJM Journal Watch. 2023. Feb 27. Available from: https://www.jwatch.org/na55829/2023/02/27/outside-box-drug-combinations-multidrug-resistant [ Google Scholar ] 390. Fiore M, Corrente A, Pace MC, Alfieri A, Simeon V, Ippolito M, et al. Ceftolozane-tazobactam combination therapy compared to ceftolozane-tazobactam monotherapy for the treatment of severe infections: a systematic review and meta-analysis. Antibiotics (Basel). 2021;10(1):79. Available from: 10.3390/antibiotics10010079 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 391. Lai C, Ma Z, Zhang J, Wang J, Wang J, Wu Z, et al. Efficiency of combination therapy versus monotherapy for the treatment of infections due to carbapenem-resistant Gram-negative bacteria: a systematic review and meta-analysis. Syst Rev. 2024;13(1):309. Available from: 10.1186/s13643-024-02695-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 392. Savoldi A, Carrara E, Piddock LJV, Franceschi F, Ellis S, Chiamenti M, et al. The role of combination therapy in the treatment of severe infections caused by carbapenem resistant Gram-negatives: a systematic review of clinical studies. BMC Infect Dis. 2021;21(1):545. Available from: 10.1186/s12879-021-06253-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 393. Righi E, Scudeller L, Chiamenti M, Abdelraouf K, Lodise T, Carrara E, et al. In vivo studies on antibiotic combination for the treatment of carbapenem-resistant Gram-negative bacteria: a systematic review and meta-analysis protocol. BMJ Open Sci. 2020;4(1):e100055. Available from: 10.1136/bmjos-2019-100055 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 394. Wang X, Wang Q, Cao B, Sun S, Zhang Y, Gu B, et al. Retrospective observational study from a Chinese network of the impact of combination therapy versus monotherapy on mortality from carbapenem-resistant Enterobacteriaceae bacteremia. Antimicrob Agents Chemother. 2019;63(1):e01511–18. Available from: 10.1128/AAC.01511-18 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 395. Sakoulas G, Rose W, Berti A, Olson J, Munguia J, Nonejuie P, et al. Classical β-lactamase inhibitors potentiate the activity of daptomycin against methicillin-resistant Staphylococcus aureus and colistin against Acinetobacter baumannii. Antimicrob Agents Chemother. 2017;61(2):e01745–16. Available from: 10.1128/AAC.01745-16 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 396. Beganovic M, Luther MK, Rice LB, Arias CA, Rybak MJ, LaPlante KL. A review of combination antimicrobial therapy for Enterococcus faecalis bloodstream infections and infective endocarditis. Clin Infect Dis. 2018;67(2):303–9. Available from: 10.1093/cid/ciy064 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 397. Geriak M, Haddad F, Rizvi K, Rose W, Kullar R, LaPlante K, et al. Clinical data on daptomycin plus ceftaroline versus standard of care monotherapy in the treatment of methicillin-resistant Staphylococcus aureus bacteremia. Antimicrob Agents Chemother. 2019;63(5):e02483–18. Available from: 10.1128/AAC.02483-18 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 398. Holubar M, Meng L, Alegria W, Deresinski S. Bacteremia due to methicillin-resistant Staphylococcus aureus : an update on new therapeutic approaches. Infect Dis Clin North Am. 2020;34(4):849–61. Available from: 10.1016/j.idc.2020.04.003 [ DOI ] [ PubMed ] [ Google Scholar ] 399. Pujol M, Miró JM, Shaw E, Aguado JM, San-Juan R, Puig-Asensio M, et al. ; MRSA Bacteremia (BACSARM) Trial Investigators . Daptomycin plus fosfomycin versus daptomycin alone for methicillin-resistant Staphylococcus aureus bacteremia and endocarditis: a randomized clinical trial. Clin Infect Dis. 2021;72(9):1517–25. Available from: 10.1093/cid/ciaa1081 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 400. Lee YC, Chen PY, Wang JT, Chang SC. A study on combination of daptomycin with selected antimicrobial agents: in vitro synergistic effect of MIC value of 1 mg/L against MRSA strains. BMC Pharmacol Toxicol. 2019;20(1):25. Available from: 10.1186/s40360-019-0305-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 401. Sakoulas G, Olson J, Yim J, Singh NB, Kumaraswamy M, Quach DT, et al. Cefazolin and ertapenem, a synergistic combination used to clear persistent Staphylococcus aureus bacteremia. Antimicrob Agents Chemother. 2016;60(11):6609–18. Available from: 10.1128/AAC.01192-16 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 402. Cañas MA, García-González J, Luque-Paz D, Cuervo G, Krivak F, Nin-Blanch J, et al. ; Hospital Clinic Endocarditis Study Group . In vitro and in vivo activity of daptomycin plus ceftaroline for the treatment of experimental endocarditis due to Enterococcus faecalis with and without high-level aminoglycoside resistance. J Antimicrob Chemother. 2026;81(5):dkag136. Available from: 10.1093/jac/dkag136 [ DOI ] [ PubMed ] [ Google Scholar ] 403. Wang C, Ye C, Liao L, Wang Z, Hu Y, Deng C, et al. Adjuvant β-lactam therapy combined with vancomycin or daptomycin for methicillin-resistant Staphylococcus aureus bacteremia: a systematic review and meta-analysis. Antimicrob Agents Chemother. 2020;64(11):e01377–20. Available from: 10.1128/AAC.01377-20 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 404. Johnson TM, Molina KC, Miller MA, Kiser TH, Huang M, Mueller SW. Combination ceftaroline and daptomycin salvage therapy for complicated methicillin-resistant Staphylococcus aureus bacteraemia compared with standard of care. Int J Antimicrob Agents. 2021;57(4):106310. Available from: 10.1016/j.ijantimicag.2021.106310 [ DOI ] [ PubMed ] [ Google Scholar ] 405. McCreary EK, Kullar R, Geriak M, Zasowski EJ, Rizvi K, Schulz LT, et al. Multicenter cohort of patients with methicillin-resistant Staphylococcus aureus bacteremia receiving daptomycin plus ceftaroline compared with other MRSA treatments. Open Forum Infect Dis. 2020;7(1):ofz538. Available from: 10.1093/ofid/ofz538 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 406. Ulloa ER, Singh KV, Geriak M, Haddad F, Murray BE, Nizet V, Sakoulas G. Cefazolin and ertapenem salvage therapy rapidly clears persistent methicillin-susceptible Staphylococcus aureus bacteremia. Clin Infect Dis. 2020;71(6):1413–8. Available from: 10.1093/cid/ciz995 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 407. Sargi J, Anwar R, Kalaji W, Adarkwah O, Mesiha N, Orsini J, et al. Synergistic effect of ertapenem to cefazolin in the therapy of pneumonia and persistent bacteremia caused by methicillin-sensitive Staphylococcus aureus (MSSA). Am J Respir Crit Care Med. 2020;201 Suppl 1:A6907. Available from: 10.1164/ajrccmconference.2020.201.1_MeetingAbstracts.A6907 [ DOI ] [ Google Scholar ] 408. Tong SYC, Lye DC, Yahav D, Sud A, Robinson JO, Nelson J, et al. Effect of vancomycin or daptomycin with vs without an antistaphylococcal β-lactam on mortality, bacteremia, relapse, or treatment failure in patients with MRSA bacteremia: a randomized clinical trial. JAMA. 2020;323(6):527–37. Available from: 10.1001/jama.2020.0103 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 409. Marr KA, Schlamm HT, Herbrecht R, Rottinghaus ST, Bow EJ, Cornely OA, et al. Combination antifungal therapy for invasive aspergillosis: a randomized trial. Ann Intern Med. 2015;162(2):81–9. Available from: 10.7326/M13-2508 [ DOI ] [ PubMed ] [ Google Scholar ] 410. Spitzer M, Robbins N, Wright GD. Combinatorial strategies for combating invasive fungal infections. Virulence. 2017;8(2):169–85. Available from: 10.1080/21505594.2016.1196300 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 411. Epelbaum O, Marinelli T, Haydour Q, Pennington KM, Evans SE, Carmona EM, et al. Treatment of invasive pulmonary aspergillosis and preventive and empirical therapy for invasive candidiasis in adult pulmonary and critical care patients: an official American Thoracic Society clinical practice guideline. Am J Respir Crit Care Med. 2025;211(1):34–53. Available from: 10.1164/rccm.202410-2045ST [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 412. Harrison TS, Walsh TJ. Time to re-evaluate combination antifungal therapy for invasive aspergillosis and other invasive mycoses. Lancet Infect Dis. 2026:S1473-3099(26)00004-6. Available from: 10.1016/S1473-3099(26)00004-6 [ DOI ] [ PubMed ] [ Google Scholar ] 413. Kumar A, Ellis P, Arabi Y, Roberts D, Light B, Parrillo JE, et al. ; Cooperative Antimicrobial Therapy of Septic Shock Database Research Group . Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest. 2009;136(5):1237–48. Available from: 10.1378/chest.09-0087 [ DOI ] [ PubMed ] [ Google Scholar ] 414. Ahmed A, Azim A, Gurjar M, Baronia AK. Current concepts in combination antibiotic therapy for critically ill patients. Indian J Crit Care Med. 2014;18(5):310–4. Available from: 10.4103/0972-5229.132495 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 415. Ripa M, Rodríguez-Núñez O, Cardozo C, Naharro-Abellán A, Almela M, Marco F, et al. Influence of empirical double-active combination antimicrobial therapy compared with active monotherapy on mortality in patients with septic shock: a propensity score-adjusted and matched analysis. J Antimicrob Chemother. 2017;72(12):3443–52. Available from: 10.1093/jac/dkx315 [ DOI ] [ PubMed ] [ Google Scholar ] 416. Zhu M, Tse MW, Weller J, Chen J, Blainey PC. The future of antibiotics begins with discovering new combinations. Ann N Y Acad Sci. 2021;1496(1):82–96. Available from: 10.1111/nyas.14649 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 417. Krishna A, Zere T, Mistry S, Ismaiel O, Stone H, Sacks LV, et al. Evaluation of a sequential antibiotic treatment regimen of ampicillin, ciprofloxacin and fosfomycin against Escherichia coli CFT073 in the hollow fiber infection model compared with simultaneous combination treatment. Antibiotics (Basel). 2022;11(12):1705. Available from: 10.3390/antibiotics11121705 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 418. Tyers M, Wright GD. Drug combinations: a strategy to extend the life of antibiotics in the 21st century. Nat Rev Microbiol. 2019;17(3):141–55. Available from: 10.1038/s41579-018-0141-x [ DOI ] [ PubMed ] [ Google Scholar ] 419. FakhriRavari A, Simiyu B, Morrisette T, Dayo Y, Abdul-Mutakabbir JC. Infectious disease: how to manage Gram-positive and Gram-negative pathogen conundrums with dual beta-lactam therapy. Drugs Context. 2022;11:2021-8-9. Available from: 10.7573/dic.2021-8-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 420. Coates ARM, Hu Y, Holt J, Yeh P. Antibiotic combination therapy against resistant bacterial infections: synergy, rejuvenation and resistance reduction. Expert Rev Anti Infect Ther. 2020;18(1):5–15. Available from: 10.1080/14787210.2020.1705155 [ DOI ] [ PubMed ] [ Google Scholar ] 421. Lázár V, Snitser O, Barkan D, Kishony R. Antibiotic combinations reduce Staphylococcus aureus clearance. Nature. 2022;610:540–6. Available from: 10.1038/s41586-022-05260-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 422. Tuon FF, Zequinao T, Telles JP. A perspective on combination of antibiotic therapy for extensively drug-resistance bacterial infections. Infect Dis Clin Pract. 2025;33(2). Available from: 10.1097/IPC.0000000000001424 [ DOI ] [ Google Scholar ] 423. Morales-Durán N, León-Buitimea A, Morones-Ramírez JR. Unraveling resistance mechanisms in combination therapy: a comprehensive review of recent advances and future directions. Heliyon. 2024;10(6):e27984. Available from: 10.1016/j.heliyon.2024.e27984 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 424. Paul M, Silbiger I, Grozinsky S, Soares-Weiser K, Leibovici L. Beta lactam antibiotic monotherapy versus beta lactam-aminoglycoside antibiotic combination therapy for sepsis. Cochrane Database Syst Rev. 2006;(1):CD003344. Available from: 10.1002/14651858.CD003344.pub2 [ DOI ] [ PubMed ] [ Google Scholar ] 425. Batchelder JI, Hare PJ, Mok WWK. Resistance-resistant antibacterial treatment strategies. Front Antibiot. 2023;2:1093156. Available from: 10.3389/frabi.2023.1093156 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 426. Angst DC, Tepekule B, Sun L, Bogos B, Bonhoeffer S. Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting. Proc Natl Acad Sci U S A. 2021;118(13):e2023467118. Available from: 10.1073/pnas.2023467118 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 427. Garimella N, Zere T, Hartman N, Gandhi A, Bekele A, Li X, et al. Effect of drug combinations on the kinetics of antibiotic resistance emergence in Escherichia coli CFT073 using an in vitro hollow-fibre infection model. Int J Antimicrob Agents. 2020;55(4):105861. Available from: 10.1016/j.ijantimicag.2019.105861 [ DOI ] [ PubMed ] [ Google Scholar ] 428. Hetta HF, Ramadan YN, Al-Kadmy IMS. Editorial for Special Issue “Antibiotic Combination Therapy: A Strategy to Overcome Bacterial Resistance”. Biomedicines. 2025;13(1):129. Available from: 10.3390/biomedicines13010129 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 429. Bognár B, Spohn R, Lázár V. Drug combinations targeting antibiotic resistance. NPJ Antimicrob Resist. 2024;2(1):29. Available from: 10.1038/s44259-024-00047-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 430. Zhang W, Jiang H, Wu G, Huang P, Wang H, An H, et al. The pathogenesis and potential therapeutic targets in sepsis. MedComm (2020). 2023;4(6):e418. Available from: 10.1002/mco2.418 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 431. Moriyama K, Nishida O. Targeting cytokines, pathogen-associated molecular patterns, and damage-associated molecular patterns in sepsis via blood purification. Int J Mol Sci. 2021;22(16):8882. Available from: 10.3390/ijms22168882 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 432. Steinhagen F, Schmidt SV, Schewe JC, Peukert K, Klinman DM, Bode C. Immunotherapy in sepsis: brake or accelerate? Pharmacol Ther. 2020;208:107476. Available from: 10.1016/j.pharmthera.2020.107476 [ DOI ] [ PubMed ] [ Google Scholar ] 433. Andersson DI, Hughes D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol. 2010;8(4):260–71. Available from: 10.1038/nrmicro2319 [ DOI ] [ PubMed ] [ Google Scholar ] 434. Stanton IC, Murray AK, Zhang L, Snape J, Gaze WH. Evolution of antibiotic resistance at low antibiotic concentrations including selection below the minimal selective concentration. Commun Biol. 2020;3(1): 467. Available from: 10.1038/s42003-020-01176-w [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 435. De Gelder L, Ponciano JM, Abdo Z, Joyce P, Forney LJ, Top EM. Combining mathematical models and statistical methods to understand and predict the dynamics of antibiotic-sensitive mutants in a population of resistant bacteria during experimental evolution. Genetics. 2004;168(3):1131–44. Available from: 10.1534/genetics.104.033431 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 436. Kettlewell R, Jones C, Felton TW, Lagator M, Gifford DR. Insights into durability against resistance from the antibiotic nitrofurantoin. NPJ Antimicrob Resist. 2024;2(1):41. Available from: 10.1038/s44259-024-00056-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 437. Larsson DGJ, Flach CF. Antibiotic resistance in the environment. Nat Rev Microbiol. 2022;20(5):257–69. Available from: 10.1038/s41579-021-00649-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 438. Oliveira RA, Cabral V, Xavier KB. Microbiome-diet interactions drive antibiotic efficacy. Nat Microbiol. 2021;6(7):824–5. Available from: 10.1038/s41564-021-00926-8 [ DOI ] [ PubMed ] [ Google Scholar ] 439. Patangia DV, Ryan CA, Dempsey E, Ross RP, Stanton C. Impact of antibiotics on the human microbiome and consequences for host health. Microbiologyopen. 2022;11(1):e1260. Available from: 10.1002/mbo3.1260 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 440. Hutchings MI, Truman AW, Wilkinson B. Antibiotics: past, present and future. Curr Opin Microbiol. 2019;51:72–80. Available from: 10.1016/j.mib.2019.10.008 [ DOI ] [ PubMed ] [ Google Scholar ] 441. Thomas JK, Forrest A, Bhavnani SM, Hyatt JM, Cheng A, Ballow CH, et al. Pharmacodynamic evaluation of factors associated with the development of bacterial resistance in acutely ill patients during therapy. Antimicrob Agents Chemother. 1998;42(3):521–7. Available from: 10.1128/AAC.42.3.521 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 442. Tam VH, Louie A, Fritsche TR, Deziel M, Liu W, Brown DL, et al. Impact of drug-exposure intensity and duration of therapy on the emergence of Staphylococcus aureus resistance to a quinolone antimicrobial. J Infect Dis. 2007;195(12):1818–27. Available from: 10.1086/518003 [ DOI ] [ PubMed ] [ Google Scholar ] 443. Drlica K, Zhao X. Mutant selection window hypothesis updated. Clin Infect Dis. 2007;44(5):681–8. Available from: 10.1086/511642 [ DOI ] [ PubMed ] [ Google Scholar ] 444. D’Agata EM, Dupont-Rouzeyrol M, Magal P, Olivier D, Ruan S. The impact of different antibiotic regimens on the emergence of antimicrobial-resistant bacteria. PLoS One. 2008;3(12):e4036. Available from: 10.1371/journal.pone.0004036 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 445. Paterson IK, Hoyle A, Ochoa G, Baker-Austin C, Taylor NG. Optimising antibiotic usage to treat bacterial infections. Sci Rep. 2016;6:37853. Available from: 10.1038/srep37853 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 446. Rinaldi M, Gatti M, Giannella M. Avoiding resistance development to newer drugs: open research lines. Clin Microbiol Infect. 2026;32:543. Available from: 10.1016/j.cmi.2025.11.009 [ DOI ] [ PubMed ] [ Google Scholar ] 447. Pasquau J, Sadyrbaeva S, De Jesús SE, Hidalgo-Tenorio C. El papel de los programas de optimización de la antibioterapia (PROA) en el control de las resistencias bacterianas [The role of antimicrobial stewardship programs in the control of bacterial resistance]. Rev Esp Quimioter. 2016;29 Suppl 1:47–51. [ PubMed ] [ Google Scholar ] 448. Dunbar LM, Milata J, McClure T, Wasilewski MM; SIMPLIFI Study Team . Comparison of the efficacy and safety of oritavancin front-loaded dosing regimens to daily dosing: an analysis of the SIMPLIFI trial. Antimicrob Agents Chemother. 2011;55(7):3476–84. Available from: 10.1128/AAC.00029-11 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 449. GOV.UK . UK Health Security Agency. Start smart and then focus: antimicrobial stewardship toolkit for inpatient care settings. Updated 12 September 2023. 450. Abdul-Aziz MH, Lipman J, Mouton JW, Hope WW, Roberts JA. Applying pharmacokinetic/pharmacodynamic principles in critically ill patients: optimizing efficacy and reducing resistance development. Semin Respir Crit Care Med. 2015;36(1):136–53. Available from: 10.1055/s-0034-1398490 [ DOI ] [ PubMed ] [ Google Scholar ] 451. Bissantz C, Zampaloni C, David-Pierson P, Dieppois G, Guenther A, Trauner A, et al. Translational PK/PD for the development of novel antibiotics: a drug developer’s perspective. Antibiotics (Basel). 2024;13(1):72. Available from: 10.3390/antibiotics13010072 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 452. Gajic I, Tomic N, Lukovic B, Jovicevic M, Kekic D, Petrovic M, et al. A comprehensive overview of antibacterial agents for combating multidrug-resistant bacteria: the current landscape, development, future opportunities, and challenges. Antibiotics (Basel). 2025;14(3):221. Available from: 10.3390/antibiotics14030221 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 453. Roger C, Koulenti D, Novy E, Roberts J, Dahyot-Fizelier C. Optimization of antimicrobial therapy in critically ill patients, what clinicians and researchers must know. Anaesth Crit Care Pain Med. 2025;44(5):101561. Available from: 10.1016/j.accpm.2025.101561 [ DOI ] [ PubMed ] [ Google Scholar ] 454. Cusack R, Little E, Martin-Loeches I. Practical lessons on antimicrobial therapy for critically ill patients. Antibiotics (Basel). 2024;13(2):162. Available from: 10.3390/antibiotics13020162 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 455. Bassetti S, Tschudin-Sutter S, Egli A, Osthoff M. Optimizing antibiotic therapies to reduce the risk of bacterial resistance. Eur J Intern Med. 2022;99:7–12. Available from: 10.1016/j.ejim.2022.01.029 [ DOI ] [ PubMed ] [ Google Scholar ] 456. Rice LB. The Maxwell Finland Lecture: for the duration: rational antibiotic administration in an era of antimicrobial resistance and Clostridium difficile . Clin Infect Dis. 2008;46(4):491–6. Available from: 10.1086/526535 [ DOI ] [ PubMed ] [ Google Scholar ] 457. Spellberg B, Rice LB. Duration of antibiotic therapy: shorter is better. Ann Intern Med. 2019;171(3):210–1. Available from: 10.7326/M19-1509 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 458. Braykov NP, Morgan DJ, Schweizer ML, Uslan DZ, Kelesidis T, Weisenberg SA, et al. Assessment of empirical antibiotic therapy optimisation in six hospitals: an observational cohort study. Lancet Infect Dis. 2014;14(12): 1220–7. Available from: 10.1016/S1473-3099(14)70952-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 459. Murri R, Taccari F, Spanu T, D’Inzeo T, Mastrorosa I, Giovannenze F, et al. A 72-h intervention for improvement of the rate of optimal antibiotic therapy in patients with bloodstream infections. Eur J Clin Microbiol Infect Dis. 2018;37(1):167–73. [ DOI ] [ PubMed ] [ Google Scholar ] 460. Pasquau J, Matesanz M. La duración del tratamiento antibiótico. Rev Esp Quimioter. 2015;28 Suppl 1:30–3. [ PubMed ] [ Google Scholar ] 461. Pasquau J, de Jesus SE, Sadyrbaeva S, Aznarte P, Hidalgo-Tenorio C. The reduction in duration of antibiotic therapy as a key element of antibiotic stewardship programs. J Antimicro. 2015;1:1–6. Available from: 10.4172/2472-1212.1000103 [ DOI ] [ Google Scholar ] 462. Spellberg B. The new antibiotic mantra: “shorter is better”. JAMA Intern Med. 2016;176(9):1254–5. Available from: 10.1001/jamainternmed.2016.3646 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 463. Grant J, Saux NL; members of the Antimicrobial Stewardship and Resistance Committee (ASRC) of the Association of Medical Microbiology and Infectious Disease (AMMI) Canada . Duration of antibiotic therapy for common infections. J Assoc Med Microbiol Infect Dis Can. 2021;6(3):181–97. Available from: 10.3138/jammi-2021-04-29 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 464. Rahmel T, Traut I, Bergmann L, Almyroudi MP, Tamowicz B, Varma P, et al. Duration matters: tailoring antibiotic therapy for ventilator-associated pneumonia. Antibiotics (Basel). 2026;15(1):34. Available from: 10.3390/antibiotics15010034 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 465. Vidaur L, Planas K, Sierra R, Dimopoulos G, Ramirez A, Lisboa T, et al. Ventilator-associated pneumonia: impact of organisms on clinical resolution and medical resources utilization. Chest. 2008;133(3):625–32. Available from: 10.1378/chest.07-2020 [ DOI ] [ PubMed ] [ Google Scholar ] 466. Luna CM, Blanzaco D, Niederman MS, Matarucco W, Baredes NC, Desmery P, et al. Resolution of ventilator-associated pneumonia: prospective evaluation of the clinical pulmonary infection score as an early clinical predictor of outcome. Crit Care Med. 2003;31(3): 676–82. Available from: 10.1097/01.CC-M.0000055380.86458.1E [ DOI ] [ PubMed ] [ Google Scholar ] 467. Montravers P, Fagon JY, Chastre J, Lecso M, Dombret MC, Trouillet JL, et al. Follow-up protected specimen brushes to assess treatment in nosocomial pneumonia. Am Rev Respir Dis. 1993;147(1):38–44. Available from: 10.1164/ajrccm/147.1.38 [ DOI ] [ PubMed ] [ Google Scholar ] 468. Spellberg B, Rice LB. Duration of antibiotic therapy: shorter is better. Ann Intern Med. 2019;171(3):210–1. Available from: 10.7326/M19-1509 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 469. Mo Y, Tan WC, Cooper BS. Antibiotic duration for common bacterial infections: a systematic review. JAC Antimicrob Resist. 2025;7(1):dlae215. Available from: 10.1093/jacamr/dlae215 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 470. Kuijpers SME, Buis DTP, Ziesemer KA, van Hest RM, Schade RP, Sigaloff KCE, et al. The evidence base for the optimal antibiotic treatment duration of upper and lower respiratory tract infections: an umbrella review. Lancet Infect Dis. 2025;25(1):94–113. Available from: 10.1016/S1473-3099(24)00456-0 [ DOI ] [ PubMed ] [ Google Scholar ] 471. Tansarli GS, Andreatos N, Pliakos EE, Mylonakis E. A systematic review and meta-analysis of antibiotic treatment duration for bacteremia due to Enterobacteriaceae. Antimicrob Agents Chemother. 2019;63:e02495–18. Available from: 10.1128/AAC.02495-18 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 472. Langford BJ, Brown KA, Lau C, Calzavara A, Krueger C, Leung V, et al. Evaluating harms associated with prolonged antibiotic duration of therapy in community-dwelling older adults: a cohort study using instrumental variable analysis. Clin Infect Dis. 2025;80(4):715–22. Available from: 10.1093/cid/ciae629 [ DOI ] [ PubMed ] [ Google Scholar ] 473. Palin V, Welfare W, Ashcroft DM, van Staa TP. Shorter and longer courses of antibiotics for common infections and the association with reductions of infection-related complications including hospital admissions. Clin Infect Dis. 2021;73(10):1805–12. Available from: 10.1093/cid/ciab159 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 474. Marino A, Augello E, Bellanca CM, Cosentino F, Stracquadanio S, La Via L, et al. Antibiotic therapy duration for multidrug-resistant Gram-negative bacterial infections: an evidence-based review. Int J Mol Sci. 2025;26(14):6905. Available from: 10.3390/ijms26146905 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 475. Lee RA, Stripling JT, Spellberg B, Centor RM. Short-course antibiotics for common infections: what do we know and where do we go from here? Clin Microbiol Infect. 2023;29(2):150–9. Available from: 10.1016/j.cmi.2022.08.024 [ DOI ] [ PubMed ] [ Google Scholar ] 476. Bassetti M, Garnacho-Montero J, Paiva JA. When antibiotic treatment fails. Intensive Care Med. 2018;44(1):73–5. Available from: 10.1007/s00134-017-4962-2 [ DOI ] [ PubMed ] [ Google Scholar ] 477. Garau J, Blasi F, Medina J, McBride K, Ostermann H; REACH study group . Early response to antibiotic treatment in European patients hospitalized with complicated skin and soft tissue infections: analysis of the REACH study. BMC Infect Dis. 2015;15:78. Available from: 10.1186/s12879-015-0822-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 478. Iwata H, Kaneko M, Aoki T, Endo K, Nagai Y, Kanto K, et al. Essential components of a definition for early antibiotic treatment failure: a scoping review. PLoS One. 2023;18(6):e0283417. Available from: 10.1371/journal.pone.0283417 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 479. Israelsen SB, Fally M, Tarp B, Kolte L, Ravn P, Benfield T. Short-course antibiotic therapy for hospitalized patients with early clinical response in community-acquired pneumonia: a multicentre cohort study. Clin Microbiol Infect. 2023;29(1):54–60. Available from: 10.1016/j.cmi.2022.08.004 [ DOI ] [ PubMed ] [ Google Scholar ] 480. O’Riordan W, McManus A, Teras J, Poromanski I, Cruz-Saldariagga M, Quintas M, et al. ; PROCEED Study Group . A comparison of the efficacy and safety of intravenous followed by oral delafloxacin with vancomycin plus aztreonam for the treatment of acute bacterial skin and skin structure infections: a phase 3, multinational, double-blind, randomized study. Clin Infect Dis. 2018;67(5):657–66. Available from: 10.1093/cid/ciy165 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 481. Helton B, MacWhinnie A, Minor SB, Lodise TP, Rafferty KD, Allison SL. Early directed oritavancin therapy in the Emergency Department may lead to hospital avoidance compared to standard treatment for acute bacterial skin and skin structure infections: a real-world retrospective analysis. Drugs Real World Outcomes. 2020;7 Suppl 1:20–9. Available from: 10.1007/s40801-020-00201-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 482. Whittaker C, Lodise TP, Nhan E, Reilly J. Expediting discharge in hospitalized, adult patients with skin and soft tissue infections who received empiric vancomycin therapy with oritavancin: description of findings from an institutional pathway. Drugs Real World Outcomes. 2020;7 Suppl 1:30–5. Available from: 10.1007/s40801-020-00196-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 483. Co D, Roebuck L, VanLandingham J. Evaluation of oritavancin use at a community hospital. Hosp Pharm. 2018;53(4):272–6. Available from: 10.1177/0018578717746415 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 484. Wurcel AG, DeSimone DC, Marks L, Baddour LM, Sendi P. Which trial do we need? Long-acting glycopeptides versus oral antibiotics for infective endocarditis in patients with substance use disorder. Clin Microbiol Infect. 2023;29(8):952–4. Available from: 10.1016/j.cmi.2023.04.005 [ DOI ] [ PubMed ] [ Google Scholar ] 485. Eckmann C, Tulkens PM. Current and future options for treating complicated skin and soft tissue infections: focus on fluoroquinolones and long-acting lipoglycopeptide antibiotics. J Antimicrob Chemother. 2021;76 Suppl 4:iv9–22. Available from: 10.1093/jac/dkab351 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 486. Lodise TP, Redell M, Armstrong SO, Sulham KA, Corey GR. Efficacy and safety of oritavancin relative to vancomycin for patients with acute bacterial skin and skin structure infections (ABSSSI) in the outpatient setting: results from the SOLO Clinical Trials. Open Forum Infect Dis. 2017;4(1):ofw274. Available from: 10.1093/ofid/ofw274 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 487. Goodman-Meza D, Weiss RE, Poimboeuf ML, Feng J, Vijayan T, Martinello M, et al. Comparative effectiveness of long-acting lipoglycopeptides vs standard-of-care antibiotics in serious bacterial infections. JAMA Netw Open. 2025;8(5):e2511641. Available from: 10.1001/jamanetworkopen.2025.11641 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 488. Pontali E, Baiardi G, Del Puente F, Mattioli F. Long-acting antibiotics: new opportunities beyond acute bacterial skin and skin structure infections (ABSSSIs)! Antibiotics (Basel). 2025;14(2):164. Available from: 10.3390/antibiotics14020164 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 489. Micheli G, Chiuchiarelli M, Taccari F, Fantoni M. The role of long-acting antibiotics in the clinical practice: a narrative review. Infez Med. 2023;31(4):449–65. Available from: 10.53854/liim-3104-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 490. Eurosurveillance editorial team . WHO member states adopt global action plan on antimicrobial resistance. Euro Surveill. 2015;20(21):21137. [ PubMed ] [ Google Scholar ] 491. European Commission . European One Health Action Plan against Antimicrobial Resistance (AMR). 2017. Available from: https://health.ec.europa.eu/system/files/2020-01/amr_2017_action-plan_0.pdf [ Google Scholar ] 492. OECD . Antimicrobial resistance in the EU/EEA: a One Health response. 2022. Available from: https://www.oecd.org/content/dam/oecd/en/publications/reports/2022/03/antimicrobial-resistance-in-the-eu-eea_146cd4e5/a213cd5a-en.pdf 493. Antimicrobial Resistance Collaborators . An agenda for all. Lancet. 2024;403(10442):2349. [ DOI ] [ PubMed ] [ Google Scholar ] 494. Laxminarayan R, Impalli I. The Lancet Series on Antimicrobial Resistance: the need for sustainable access to effective antibiotics. One Health Trust. 2024. Jun 7. Available from: https://onehealthtrust.org/publications/peer-reviewed-articles/the-lancet-series-on-antimicro-bial-resistance-the-need-for-sustainable-access-to-effective-antibiotics/ [ Google Scholar ] 495. Marín-Candón A, Rosso-Fernández CM, Bustos de Godoy N, et al. Temocillin versus meropenem for the targeted treatment of bacteraemia due to third-generation cephalosporin-resistant Enterobacterales (ASTARTÉ): protocol for a randomised, pragmatic trial. BMJ Open. 2021;11:e049481. Available from: 10.1136/bmjopen-2021-049481 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 496. Beasley JM, Dorjsuren D, Jain S, Rath M, Scheufen Tieghi R, Tropsha A, et al. Breaking the phalanx: overcoming bacterial drug resistance with quorum sensing inhibitors that enhance therapeutic activity of antibiotics. bioRxiv. 2025. Jun 23:2025.01.17.633658. Preprint. Available from: 10.1101/2025.01.17.633658 [ DOI ] [ Google Scholar ] 497. Xu J, Li C, Wang X, Peleg AY, Li F, Song J, et al. Artificial intelligence in antibiotic discovery: applications, challenges, and future outlook. Cells Biomater. 2026:100445. Available from: 10.1016/j.celbio.2026.100445 [ DOI ] [ Google Scholar ] 498. Hatim AN, Ammar Abdul Razzak M, Sriram T, Rajkumar Krishnan V, Desh NS. Artificial intelligence in combating antimicrobial resistance. Arch Razi Inst. 2025;80(3): 605–13. Available from: 10.32592/ARI.2025.80.3.605 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 499. Salama RA, Abdel Kader RG, Wadid NA. Artificial intelligence in combating challenges in antimicrobial resistance: a narrative review. Infect Prev Pract. 2026;8(2):100522. Available from: 10.1016/j.infpip.2026.100522 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 500. Arnold A, McLellan S, Stokes JM. How AI can help us beat AMR. NPJ Antimicrob Resist. 2025;3(1):18. Available from: 10.1038/s44259-025-00085-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 501. Sanapalli BKR, Palit S, Deshpande A, Tokala R, Sigalapalli DK, Sanapalli V. Artificial intelligence and the discovery of antibiotics: reinventing with opportunities, challenges, and clinical translation. Antibiotics (Basel). 2026;15(2):233. Available from: 10.3390/antibiotics15020233 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 502. Howard A, Reza N, Green PL, Yin M, Duffy E, Mwandumba HC, et al. Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery. Lancet Infect Dis. 2026;26(3):e181–92. Available from: 10.1016/S1473-3099(25)00313-5 [ DOI ] [ PubMed ] [ Google Scholar ] 503. The AMR brain drain . Lancet Microbe. 2024;5(5):e409. Available from: 10.1016/S2666-5247(24)00102-2 [ DOI ] [ PubMed ] [ Google Scholar ] 504. Schurer M, et al. Recent advances in addressing the market failure of new antimicrobials: learnings from NICE’s subscription-style payment model. Front Med Technol. 2023;5:1010247. Available from: 10.3389/fmedt.2023.1010247 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 505. Anderson M, Panteli D, Mossialos E. How can the EU support sustainable innovation and access to effective antibiotics? Policy options for existing and new medicines. Copenhagen: European Observatory on Health Systems and Policies; 2023. [ PubMed ] [ Google Scholar ] 506. Anderson M, Panteli D, Mossialos E. Strengthening the EU response to prevention and control of antimicrobial resistance. Policy priorities for effective implementation. Policy Brief, No. 63. Copenhagen: European Observatory on Health Systems and Policies; 2024. [ PubMed ] [ Google Scholar ] 507. Anderson M, Ljungqvist G, van Kessel R, Saint V, Mossialos E. The socioeconomic drivers and impacts of antimicrobial resistance: implications for policy and research. Copenhagen: European Observatory on Health Systems and Policies; 2024. [ PubMed ] [ Google Scholar ] 508. Laxminarayan R, Impalli I, Rangarajan R, Cohn J, Ramjeet K, Trainor B, et al. Expanding antibiotic, vaccine, and diagnostics development and access to tackle antimicrobial resistance. Lancet. 2024;403:2534–50. [ DOI ] [ PubMed ] [ Google Scholar ] 509. US Food and Drug Administration . Antibacterial therapies for patients with an unmet medical need for the treatment of serious bacterial diseases: questions and answers. Guidance for industry. Accessed 2 June 2026. Available from: https://www.fda.gov/media/158589/download 510. Sanford Guide . Antimicrobial stewardship metrics. Accessed 3 May 2026. Available from: https://www.sanfordguide.com/antimicrobial-stewardship-metrics/ 511. The Joint Commission . New and revised requirements for antibiotic stewardship. Accessed 2 February 2023. Available from: https://www.jointcommission.org/standards/r3-report/r3-report-issue-35-new-and-revised-requirements-for-antibiotic-stewardship/ 512. Peltan ID, Brown SM, Bledsoe JR, Sorensen J, Samore MH, Allen TL, et al. ED door-to-antibiotic time and long-term mortality in sepsis. Chest. 2019;155(5):938–46. Available from: 10.1016/j.chest.2019.02.008 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 513. Leung LY, Huang HL, Hung KK, Leung CY, Lam CC, Lo RS, et al. Door-to-antibiotic time and mortality in patients with sepsis: systematic review and meta-analysis. Eur J Intern Med. 2024;129:48–61. Available from: 10.1016/j.ejim.2024.06.015 [ DOI ] [ PubMed ] [ Google Scholar ] 514. Lima APS, Nangino GO, Soares FFR, Xavier JC, Martins MC, Leite AS. Risk classification and door-to-antibiotic time in patients with suspected sepsis. Rev Lat Am Enfermagem. 2023;31:e4064. Available from: 10.1590/1518-8345.6635.4064 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 515. Chau EYW, Bakar AA, Zamhot AB, Zaini IZ, Binti Adanan SN, Sabardin DMB. An observational study on the impact of overcrowding towards door-to-antibiotic time among sepsis patients presented to emergency department of a tertiary academic hospital. BMC Emerg Med. 2024;24(1):58. Available from: 10.1186/s12873-024-00973-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 516. Nauclér P, Huttner A, van Werkhoven CH, Singer M, Tattevin P, Einav S, et al. Impact of time to antibiotic therapy on clinical outcome in patients with bacterial infections in the Emergency Department: implications for antimicrobial stewardship. Clin Microbiol Infect. 2021;27(2):175–81. Available from: 10.1016/j.cmi.2020.02.032 [ DOI ] [ PubMed ] [ Google Scholar ] 517. Hsieh MS, Chiu KC, Liao SH, Hsieh VC, Hu SY, How CK. Optimal timing of antibiotics administration for sepsis or septic shock in the Emergency Department. BMC Emerg Med. 2026;26(1):47. Available from: 10.1186/s12873-026-01471-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 518. Marshall JC, Maier RV, Jimenez M, Dellinger EP. Source control in the management of severe sepsis and septic shock: an evidence-based review. Crit Care Med. 2004;32(11 Suppl):S513–26. Available from: 10.1097/01.ccm.0000143119.41916.5d [ DOI ] [ PubMed ] [ Google Scholar ] 519. Julián-Jiménez A, Lorenzo Álvarez R, Gutiérrez Bueno V, Sánchez Trujillo M, Eduardo García D. Control precoz del foco de infección en los pacientes atendidos en el servicio de urgencias: una revisión sistemática. Rev Esp Quimioter. 2024;37(4):323–33. Available from: 10.37201/req/027.2024 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 520. Cercenado E. Rapid techniques for therapeutic optimization. Diagnostic stewardship. Rev Esp Quimioter. 2022;35 Suppl 3:80–3. Available from: 10.37201/req/s03.17.2022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 521. Bou G, Cantón R, Martínez-Martínez L, Navarro D, Vila J. Fundamentals and implementation of microbiological diagnostic stewardship programs. Enferm Infecc Microbiol Clin (Engl Ed). 2021;39(5):248–51. Available from: 10.1016/j.eimc.2020.02.019 [ DOI ] [ PubMed ] [ Google Scholar ] 522. González-Del Castillo J, Domínguez-Bernal C, Gutiérrez-Martín MC, Núñez-Orantos MJ, Candel FJ, Martín-Sánchez FJ. Effect of the inadequacy of antibiotic therapy in the Emergency Department on hospital stays. Enferm Infecc Microbiol Clin. 2017;35(4):208–13. Available from: 10.1016/j.eimc.2015.10.005 [ DOI ] [ PubMed ] [ Google Scholar ] Articles from Revista Española de Quimioterapia are provided here courtesy of Sociedad Española de Quimioterapia ACTIONS View on publisher site PDF (3.4 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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