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Learn more: PMC Disclaimer | PMC Copyright Notice J Clin Med . 2026 Mar 26;15(7):2543. doi: 10.3390/jcm15072543 Search in PMC Search in PubMed View in NLM Catalog Add to search Single Cancer Center Experience on Patient Blood Management Eligibility in Oncological Surgery Camilla L’Acqua Camilla L’Acqua 1 Anesthesia and Intensive Care Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (C.L.); [email protected] (R.L.) Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing Find articles by Camilla L’Acqua 1, † , Roberto Lillini Roberto Lillini 2 Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (R.L.); [email protected] (P.B.) Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing Find articles by Roberto Lillini 2, † , Rosamaria Limuti Rosamaria Limuti 1 Anesthesia and Intensive Care Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (C.L.); [email protected] (R.L.) Data curation, Investigation, Writing – review & editing Find articles by Rosamaria Limuti 1 , Flavio Arienti Flavio Arienti 3 Immunohematology and Transfusion Medicine Service, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] Investigation, Writing – review & editing, Supervision Find articles by Flavio Arienti 3 , Chiara Maura Ciniselli Chiara Maura Ciniselli 4 Bioinformatics and Biostatistics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (C.M.C.); [email protected] (P.V.) Methodology, Investigation, Writing – review & editing Find articles by Chiara Maura Ciniselli 4 , Paolo Verderio Paolo Verderio 4 Bioinformatics and Biostatistics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (C.M.C.); [email protected] (P.V.) Methodology, Investigation, Writing – review & editing, Supervision Find articles by Paolo Verderio 4 , Ilaria Cavallo Ilaria Cavallo 5 Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] Investigation, Writing – review & editing Find articles by Ilaria Cavallo 5 , Paolo Baili Paolo Baili 2 Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (R.L.); [email protected] (P.B.) Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing Find articles by Paolo Baili 2, ‡ , Giulia Perrone Giulia Perrone 3 Immunohematology and Transfusion Medicine Service, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing Find articles by Giulia Perrone 3, *, ‡ Editor: Won Ho Kim Author information Article notes Copyright and License information 1 Anesthesia and Intensive Care Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (C.L.); [email protected] (R.L.) 2 Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (R.L.); [email protected] (P.B.) 3 Immunohematology and Transfusion Medicine Service, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] 4 Bioinformatics and Biostatistics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] (C.M.C.); [email protected] (P.V.) 5 Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy; [email protected] * Correspondence: [email protected] † These authors contributed equally as co-first authors. ‡ These authors contributed equally as co-last authors. Roles Camilla L’Acqua : Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing Roberto Lillini : Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing Rosamaria Limuti : Data curation, Investigation, Writing – review & editing Flavio Arienti : Investigation, Writing – review & editing, Supervision Chiara Maura Ciniselli : Methodology, Investigation, Writing – review & editing Paolo Verderio : Methodology, Investigation, Writing – review & editing, Supervision Ilaria Cavallo : Investigation, Writing – review & editing Paolo Baili : Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing Giulia Perrone : Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing Won Ho Kim : Academic Editor Received 2026 Feb 10; Revised 2026 Mar 19; Accepted 2026 Mar 22; Collection date 2026 Apr. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license . PMC Copyright notice PMCID: PMC13072742 PMID: 41976845 Abstract Background: Accurate identification of patients at high risk of perioperative blood transfusion is essential for optimizing patient blood management (PBM) strategies in oncological surgery. However, the performance of standard PBM eligibility criteria in real-world oncological settings remains incompletely characterized. Material and Methods: We conducted a retrospective, single-center analysis of 4228 consecutive patients undergoing elective oncological surgery of any complexity or liver transplantation over a 9-month period to assess transfusion need and estimate access to preoperative patient blood management (PBM) strategies to improve anemia management. Transfusion events were assessed within 24 h after surgery (PS24) and during the perioperative period (PO; 48 h before to 72 h after surgery). Two PBM eligibility strategies were applied to the same patient cohort and compared: (A) an observational approach, based on predefined PBM indicators (transfusion rate and transfusion index by surgical complexity), and (B) a multivariable modeling approach based on pre- and intraoperative anesthesiology assessment to estimate individual transfusion risk. Predictive performance of both strategies was evaluated using accuracy, Cramér’s V, area under the receiver-operating characteristic curve (AUC-ROC), and Brier score. Results: Overall, 7.7% of patients received transfusion within PS24 and 9.2% during PO. According to the observational approach, 23.8% of patients were classified as PBM-eligible, accounting for 89.2% of PS24 transfusions and 87.1% of PO transfusions. In the multivariable modeling approach, independent predictors of transfusion included surgical type (e.g., sarcoma surgery: OR 22.8 for PS24; OR 6.3 for PO; vs. senology surgery OR 1 for PS24; OR 1 for PO, respectively), anemia severity (moderate anemia: OR 64.3 and OR 107.9, respectively and mild anemia OR 3.38 and OR 3.65, respectively), high surgical complexity, operative time >3 h (>3 h: OR 8.83 and OR 8.65, respectively vs. <3 h OR 1 and OR 1, respectively), and ICU admission risk. The observational approach demonstrated stronger alignment with actual transfusion events (Cramér’s V = 0.44–0.47) and higher overall accuracy (90.8–92.3%); in contrast, a multivariable modeling approach showed superior discrimination (AUC = 0.94–0.95) and lower Brier scores, indicating better individual risk prediction. Conclusions: In a large real-world cohort of oncological surgical patients, standard PBM eligibility criteria effectively identified the majority of patients requiring perioperative transfusion. While multivariable modeling provided greater predictive precision, the observational PBM approach demonstrated strong clinical alignment and practical applicability. Integrating both strategies may support more effective transfusion risk stratification and PBM planning in oncological surgery. Keywords: patient blood management, oncological surgery, anemia, transfusion risk, perioperative care 1. Introduction Preoperative anemia is a highly prevalent and clinically significant comorbidity, associated with adverse clinical outcomes, including increased morbidity and mortality, prolonged hospitalization, and impaired quality of life [ 1 ]. Although allogeneic blood transfusion remains a cornerstone in managing cancer-related anemia, accumulating evidence indicates that transfusion exposure constitutes an independent risk factor for reduced overall survival [ 2 , 3 ]. Multiple studies consistently demonstrated an association between perioperative transfusion and increased mortality, irrespective of cancer stage or therapeutic intent [ 4 ]. The adverse prognostic impact of transfusion is hypothesized to result from several mechanisms, including transfusion-related immunomodulation, increased susceptibility to infectious complications and other transfusion-associated adverse events. These findings emphasize the importance of implementing a restrictive and evidence-based transfusion approach, while reinforcing the clinical utility of patient blood management (PBM) programs in oncological care [ 5 , 6 , 7 ]. PBM is a multidisciplinary evidence-based framework to optimize patients’ blood resources. Its core pillars include identifying and treating preoperative anemia, minimizing perioperative blood loss, and enhancing patient’s physiological tolerance to anemia [ 6 , 8 , 9 , 10 ]. Robust evidence from multicenter observational cohorts and randomized controlled trials demonstrated that implementing PBM programs leads to improved clinical outcomes and decreased healthcare resource utilization, particularly in the perioperative period. Nonetheless, despite endorsement by the World Health Organization, the systematic integration of PBM into oncological surgical care remains limited and frequently underutilized [ 11 ]. This study reports the baseline assessment and methodological framework of a patient blood management (PBM) initiative implemented at the Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy. The primary objective was to evaluate whether standardized criteria for preoperative PBM inclusion can reliably identify patients undergoing oncological surgery who are at an elevated risk of perioperative allogeneic blood transfusion. To assess transfusion risk, we employed and compared two approaches: an “observational approach” based on traditional PBM indicators, such as the transfusion rate and transfusion index, and a “multivariable modeling approach” incorporating patient-specific clinical characteristics and procedural complexity. 2. Materials and Methods This retrospective, single-center study analyzed blood transfusion practices in elective oncological surgeries and liver transplant procedures performed between 26 September 2023 and 30 June 2024 at INT. Clinical and administrative data were extracted from the institutional data warehouse (DWH), allowing comprehensive tracking of surgical pathways. Data sources included surgical scheduling systems, hospital admissions records, laboratory results, anesthetic evaluations and transfusion records. Oncological surgeries were categorized by anatomical site and grouped into macro-categories according to their Surgical Complexity Score and bleeding risk, based on the 2018 classification proposed in the Position Paper of the Italian Society of Anesthesia and Intensive Care (SIAARTI) [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Anemia severity was defined according to the National Comprehensive Cancer Network (NCCN) classification ( https://www.nccn.org/guidelines ; accessed on 21 March 2026). Blood transfusions were evaluated within two distinct time frames: (a) early postoperative period: within 24 h after surgery (PS24), and (b) perioperative period: from 48 h before to 72 h after surgery (PO). All analyses were performed on consecutive patients. The same cohort was evaluated using two different PBM eligibility strategies (observational vs. multivariable modeling approach). No theoretical or simulated groups and no before–after comparisons were performed. Multivariable modeling was conducted using a complete-case (listwise deletion) approach. For anemia, cases with a missing anemia grade were excluded from the GLM. Similarly, observations with missing values for BMI, METS, dyspnea, and other covariates were excluded via listwise deletion. (1) Approach A: Observational approach For each oncological surgery, the eligibility to PBM was based on the estimated surgical transfusion risk (STR) and presence of anemia (Hb < 130 g/L). Within each surgical specialty, a classification of surgical procedures was performed based on anatomical site, bleeding risk and surgical complexity score, as defined by the surgeon. In Table S1 , for every specialty, a list of macro-categories is labeled ( Supplementary Table S1 ). To define the estimate STR, we evaluate the following key indicators of transfusion practices within each surgical macro-category and for each defined time frame (PS24 and PO): Transfusion Rate (TR): proportion of patients receiving at least one red blood cell (RBC) unit during the observation period, relative to the total number of patients undergoing the same surgical procedure. TR values were categorized as low (< 5%), intermediate (5–30%), or high (>30%) [ 19 , 20 ]. Transfusion Index (TI): average number of RBC units transfused per patient, including both transfused and non-transfused patients. A TI value > 0.3 during the perioperative period (PO) was considered indicative of high blood consumption [ 19 , 20 ]. These indicators were calculated by surgical macro-category and for each defined time frame ( Supplementary Table S1 ). TR within 24 h after surgery (TR-PS24) and TI during the perioperative period (TI-PO) were used to estimate the surgical transfusion risk (STR), which was classified as follows: High-risk: TR-PS24 > 30% in all patients. Intermediate-risk: TR-PS24 between 5 and 30% in all patients or TI-PO > 0.3 among anemic patients. Low-risk: TR-PS24 < 5% in all patients. Based on this classification, eligibility for preoperative patient blood management (PBM) interventions was defined as follows: All patients undergoing high-risk surgeries. Patients with anemia of any grade (Hb < 130 g/L) undergoing intermediate-risk surgeries. Patients with moderate or severe anemia (Hb < 100 g/L) for low-risk surgeries. (2) Approach B: Multivariable modeling approach All clinical variables collected from DWH were considered for analysis. The statistical workflow comprised the following steps: univariate analysis, bivariate comparisons; Chi-square (χ 2 ) tests were applied to categorical variables, and Student’s T -tests were used for continuous variables. To assess additional associations, univariate logistic regression analyses were performed for each dependent variable [ 21 ]. Subsequently, a backward stepwise generalized linear model (GLM) was constructed, employing a binomial distribution and a log link function to identify the strongest predictors of transfusion. For each covariate retained in the final model, odds ratios (ORs) with 95% confidence intervals were calculated [ 21 ]. Multicollinearity was assessed using variance inflation factors (VIFs). No significant collinearity was observed (all VIF values < predefined threshold) [ 21 ], supporting inclusion of the selected covariates in the final model (see Supplementary Table S2 ). To assess whether the two groups of excluded cases for missing values in the variables and included cases differed systematically in their observed characteristics, we estimated a multivariable logistic regression model in which group membership was regressed on the full set of quantitative and categorical covariates. Unlike univariate comparisons (e.g., T -tests or chi-square tests), a multivariable approach allows the joint distribution of covariates to be considered simultaneously and accounts for potential confounding across characteristics [ 22 , 23 ]. This approach provides a more comprehensive assessment of between-group differences, as it evaluates whether groups differ conditionally on the entire set of observed covariates rather than on each characteristic separately. If the variables are not significantly associated with the groups, then the groups are comparable; if the variables are significantly associated with the group, then the groups are not comparable to each other via those variables and, therefore, are different. In our case, we need to verify whether the groups are comparable to confirm that excluding missing cases does not alter the results. (3) Comparison between approaches A and B Chi-square tests were used to assess statistical associations, and Cramér’s V was calculated to measure the strength of these associations [ 21 ]. Approach A reflects current PBM practice based on the transfusion rate and index derived from historical data, whereas approach B estimates the individual transfusion probability using multivariable regression. Finally, to validate approach A, the accuracy score, Brier score [ 24 ] and AUC-ROC were also computed for the combined dataset of observed transfused patients and PBM eligibility [ 21 ]. The accuracy score reflects the proportion of correctly classified patients according to a predefined decision threshold and may be influenced by outcome prevalence. In contrast, the area under the receiver operating characteristic curve (AUC) measures discrimination, namely the ability of a model to correctly rank patients according to an increasing transfusion risk, independently of a specific cutoff. The Brier score evaluates the accuracy of probabilistic predictions by quantifying the mean squared difference between predicted probabilities and observed outcomes, thus incorporating elements of both discrimination and calibration. The accuracy score is calculated by dividing the number of correct predictions by the total prediction number. As you can see, accuracy can be easily described using the confusion matrix terms true positive, true negative, false positive, and false negative. Then, the Brier score computes the Yates, Sanders, and Murphy decompositions of the Brier mean probability score. The Brier score is a measure of disagreement between the observed outcome and a forecast (prediction). Therefore, while accuracy is appropriate for evaluating binary eligibility strategies such as the observational PBM approach, the AUC and Brier score are more informative when assessing individualized probabilistic risk prediction models [ 21 , 24 ]. All the analyses were performed by considering a significance alpha level of 0.05 with Stata MP 17.0 software. 3. Results During a 9-month observational period, 4228 consecutive patients undergoing oncological surgery or liver transplantation at INT were evaluated, included 49 pediatric patients (0–18 years old; 1.2% of all patients). For each patient, the type of surgery, estimated surgical transfusion risk (STR) and presence of preoperative anemia were assessed in relation to actual transfusion requirements ( Table 1 ). Table 1. Patient characteristics. No Transfusion Transfusion: PS24 Transfusion: PO Totals Avg. Std. Dev. Avg. Std. Dev. Avg. Std. Dev. Avg. Std. Dev. Age 60.2 15.7 61.3 15.1 62.1 15.4 60.4 15.7 N % N % N % N % Gender M 1376 35.8 142 43.7 163 42.0 1539 36.4 F 2464 64.2 183 56.3 225 58.0 2689 63.6 Anemia No anemia 2637 68.7 79 24.3 97 25.0 2734 64.7 Yes anemia 1203 31.3 246 75.7 291 75.0 1494 35.3 Type of Surgery Colon–rectum surgery 221 5.8 56 17.2 70 18.0 291 6.9 Hepato-gastro-pancreatic surgery 254 6.6 60 18.5 69 17.8 323 7.6 Melanoma surgery 411 10.7 0 0.00 2 0.5 413 9.8 Eye surgery 132 3.4 0 0.00 0 0.0 132 3.1 Plastic and reconstructive surgery 438 11.4 5 1.5 9 2.3 447 10.6 Sarcoma surgery 201 5.2 73 22.5 78 20.1 279 6.6 Thoracic surgery 315 8.2 37 11.4 44 11.3 359 8.5 Gynecology 379 9.9 50 15.4 61 15.7 440 10.4 Otorhinolaryngology/maxillofacial surgery 264 6.9 8 2.5 15 3.9 279 6.6 Pediatrics 50 1.3 5 1.5 6 1.6 56 1.3 Senology 752 19.6 1 0.3 2 0.5 754 17.8 Urology 423 11.0 30 9.2 32 8.3 455 10.8 Surgical transfusion risk (STR) High-Risk 189 4.9 160 49.2 174 44.9 363 8.6 Intermediate-Risk 1384 36.0 154 47.4 195 50.3 1579 37.4 Low-Risk 2267 59.0 11 3.4 19 4.9 2286 54.1 Preoperative PBM eligibility No 3170 82.6 35 10.8 50 12.9 3220 76.2 Yes 670 17.4 290 89.2 338 87.1 1008 23.8 Totals 3840 90.8 325 7.7 388 9.2 4228 100.0 Open in a new tab PS24: Early postoperative period: within 24 h after surgery. PO: Perioperative period: from 48 h before to 72 h after surgery. Transfusion events were categorized as no transfusion, transfusion PS24, and transfusion during PO. The mean age of the overall cohort was approximately 60 years. Regarding STR, 54.1% of all surgeries was categorized as low-risk, 37.4% as intermediate-risk, and 8.6% as high-risk. Surgical procedures associated with high STR (e.g., specifically colorectal, hepato-gastro-pancreatic, sarcoma surgeries) comprised a disproportionately large share of transfused cases (17–22%), despite accounting for a smaller portion of total surgeries. Preoperative anemia was a strong independent predictor of transfusion: approximately 75% of patients who received PO transfusions were anemic. Finally, 23.8% of patients met the criteria for inclusion in a PBM program based on an observational approach. Remarkably, this subgroup accounted for 87.1% of all PO transfusions. Analysis of quantitative variables from the preoperative anesthesiology assessments revealed several statistically significant associations with transfusion requirements, as shown in Table 2 . Table 2. Association between observed transfusions and patients’ characteristics (continuous variables). Dependent Variable: Transfusion: PS24 Dependent Variable: Transfusion: PO Variable Mean Min. Max. SD Missing (N) T -Test p -Value T -Test p ( p < 0.05) Age 60.37 2.86 101.16 15.66 0 −1.06 0.290 −2.29 0.022 Days between anesthesiology visit and surgery 18.71 0.00 120.00 18.34 60 3.69 <0.001 4.42 <0.001 Weight 69.37 13.00 144.00 15.59 68 2.74 0.006 3.11 0.002 Max. arterial pressure 133.03 78.00 207.00 20.58 250 2.97 0.003 3.27 0.001 Min. arterial pressure 83.75 50.00 802.00 15.46 250 3.37 0.001 3.87 <0.001 Heart rate (HR) 75.15 6.00 140.00 12.63 259 −5.75 <0.001 −5.68 <0.001 Open in a new tab PS24: Early postoperative period: within 24 h after surgery. PO: Perioperative period: from 48 h before to 72 h after surgery. Higher age was significantly associated (association checked by T -test) with PO transfusion ( p = 0.022), while no statistically significant association was observed between age and PS24 ( p = 0.290). Statistically significant associations were observed between the majority of categorical clinical variables and transfusion requirements (see Table 3 ). Table 3. Association rates by odds ratios (OR) between observed transfusions and patients’ characteristics (bivariate logistic regression models between transfusion as dependent variable and the descriptive categorical variables as covariates). Dependent Variable: Transfusion: PS24 Dependent Variable: Transfusion: PO Freq. Percent OR p OR p Gender F 2689 63.6 1 (ref.) 0.005 0.77 0.017 M 1539 36.4 0.72 1 (ref.) Body mass index (BMI) Underweight 139 3.3 1.58 0.006 1.48 0.005 Normal–Overweight 3462 81.9 1 (ref.) 1 (ref.) Obese 559 13.2 0.59 0.60 Missing 68 1.6 Metabolic equivalent level (METS) <4 187 4.4 1 (ref.) 0.015 1 (ref.) 0.000 >4 3964 93.8 0.55 0.41 Missing 77 1.8 Dyspnea No 3091 73.1 1 (ref.) 0.011 1 (ref.) 0.003 Heavy exertion 586 13.9 1.39 1.25 Moderate exertion 411 9.7 1.66 1.80 At rest 12 0.3 2.85 2.32 Missing 128 3.0 Grade of anemia No anemia 2390 56.5 1 (ref.) 0.000 1 (ref.) 0.000 Severe anemia 7 0.2 208.03 1 (empty) Moderate anemia 150 3.6 53.48 64.45 Mild anemia 1337 31.6 4.35 4.44 Anemia not known 344 8.1 1.25 1.39 Anemia No anemia 2734 64.7 1 (ref.) 0.000 1 (ref.) 0.000 Yes anemia 1494 35.3 6.62 6.58 Anesthetic risk (ASA) I 279 6.6 1 (ref.) 0.000 1 (ref.) 0.000 II 2889 68.3 6.40 7.55 III 979 23.2 11.41 14.97 IV 21 0.5 69.00 83.64 Missing 60 1.4 ICU admission risk No 3622 85.7 1 (ref.) 0.000 1 (ref.) 0.000 Yes 456 10.8 13.47 12.14 Missing 150 3.5 Surgical category Election 4195 99.2 1 (ref.) 0.000 1 (ref.) 0.000 Transplant 33 0.8 15.20 14.07 Surgical complexity score Low 1729 40.9 1 (ref.) 0.000 1 (ref.) 0.000 Medium 1843 43.6 13.74 10.29 High 590 14.0 49.05 36.90 Not applicable 1 0.0 1 (empty) 1 (empty) Missing 65 1.5 Type of surgery Colon–rectum surgery 291 6.9 179.44 0.000 119.10 0.000 Hepato-gastro-pancreatic surgery 323 7.6 171.79 102.14 Melanoma surgery 413 9.8 1 (empty) 1.83 Eye surgery 132 3.1 1 (empty) 1 (empty) Plastic and reconstructive surgery 447 10.6 8.52 7.73 Sarcoma surgery 279 6.6 266.84 145.91 Thoracic surgery 359 8.5 86.52 52.52 Gynecology 440 10.4 96.54 60.52 Otorhinolaryngology/maxillofacial surgery 279 6.6 22.23 21.36 Pediatrics 56 1.3 73.82 45.12 Senology 754 17.8 1 (ref.) 1 (ref.) Urology 455 10.8 53.15 28.44 Duration of surgery Up to 3 h 2683 63.5 1 (ref.) 0.000 1 (ref.) 0.000 Over 3 h 1545 36.5 23.51 17.65 Open in a new tab PS24: Early postoperative period: within 24 h after surgery. PO: Perioperative period: from 48 h before to 72 h after surgery. ICU: Intensive care unit. Sarcoma, gynecology and colorectal surgeries had the highest transfusion rates, compared to surgeries with lower complexity, such as senology. Also, anemia, the ASA score, a longer surgery duration (>3 h), and the surgical complexity score were strongly associated with transfusion as expected. Multivariable logistic regression (approach B) identified several independent predictors significantly associated with transfusion requirements, both PS24 and PO ( Table 4 ). Table 4. Model approach: Final backward generalized linear model. Dependent Variable: Transfusion: PS24 Dependent Variable: Transfusion: PO Covariates Odds Ratio p 95% Lower CI 95% Upper CI Odds Ratio p 95% Lower CI 95% Upper CI Gender M 1 (ref.) 1 (ref.) F 0.56 0.011 0.36 0.88 0.63 0.035 0.41 0.97 Grade of anemia No anemia 1 (ref.) 1 (ref.) Severe anemia 61.28 0.002 4.69 800.56 Moderate anemia 64.30 <0.001 33.78 122.38 107.90 <0.001 54.89 212.11 Mild anemia 3.38 <0.001 2.34 4.89 3.65 <0.001 2.57 5.19 ICU admission risk No 1 (ref.) 1 (ref.) Yes 5.33 <0.001 3.69 7.70 5.09 <0.001 3.55 7.29 Surgical complexity score Low 1 (ref.) 1 (ref.) Medium 5.77 <0.001 2.21 15.09 3.64 <0.001 1.77 7.47 High 10.31 <0.001 3.71 28.65 6.23 <0.001 2.83 13.71 Type of surgery Senology 1 (ref.) 1 (ref.) Colon-rectal surgery 7.71 <0.001 3.24 18.35 2.48 <0.001 1.57 3.92 Hepato-gastro-pancreatic surgery 3.13 0.013 1.27 7.71 - Plastic and reconstructive surgery 7.84 0.003 2.06 29.80 4.08 0.001 1.74 9.58 Sarcoma surgery 22.83 <0.001 9.22 56.51 6.29 <0.001 3.74 10.58 Thoracic surgery 3.92 0.004 1.53 10.02 Gynecology 17.10 <0.001 6.84 42.81 4.89 <0.001 2.91 8.21 Urology 4.32 0.002 1.71 10.88 Duration of surgery Up to 3 h 1 (ref.) 1 (ref.) Over 3 h 8.83 <0.001 4.96 15.72 8.65 <0.001 5.14 14.58 Weight 0.98 <0.001 0.96 0.99 0.98 <0.001 0.97 0.99 Heart Rate (HR) 1.02 0.027 1.00 1.03 1.02 0.014 1.00 1.03 Age 1.02 0.001 1.01 1.03 Constant 0.00 <0.001 0.00 0.00 0.00 <0.001 0.00 0.00 Open in a new tab PS24: Early postoperative period: within 24 h after surgery. PO: Perioperative period: from 48 h before to 72 h after surgery. ICU: Intensive care unit. The final multivariable model included N = 3405 patients, corresponding to approximately 80.5% of the full cohort (N = 4228). Anemia severity was the most powerful clinical predictor. Patients with moderate anemia had significantly higher odds of transfusion (OR = 64.3 [33.8–122.4] and OR = 107.9 [54.9–212.1]). Severe anemia was associated with extremely high odds of transfusion (OR = 61.3 [4.7–800.6] for PS24 transfusion). Among surgical variables, the type of surgery emerged as a strong determinant of transfusion risk. When comparing the types of surgery with a low risk of transfusion (i.e., senology), the highest odds were observed for sarcoma surgery (OR = 22.8 [95% CI: 9.2–56.5] for PS24 transfusion; OR = 6.3 [3.7–10.6] for PO transfusion), followed by gynecological surgery (OR = 17.1 [6.8–42.8] and OR = 4.9 [2.9–8.2]). Other strong predictors included a high surgical complexity score (OR = 10.3 [3.7–28.6] and OR = 6.2 [2.8–13.7] for high vs. low), duration of surgery > 3 h (OR = 8.8 [5.0–15.7] and OR = 8.6 [5.1–14.6]), and ICU admission risk (OR = 5.3 [3.7–7.7] and OR = 5.1 [3.6–7.3]). Taken together, these results validate the univariate analyses described above and confirm that anemia severity, surgical type and complexity, and surgical duration are the strongest independent predictors of transfusion in oncological surgery. Missing values have no effect on the consistency of the results ( Supplementary Table S3 ). In fact, there was no evidence of structural differences between the group of missing cases, originally excluded from the GLM model, and the included cases (almost all the characteristics presented p > 0.05). Finally, a comparative analysis was conducted to evaluate the predictive performance of the approaches A and B in identifying patients requiring transfusion. The approach A identified 89.2% (290/325) of transfused patients in the PS24, as compared to 82.2% (267/325) by approach B. Contingency analysis ( Table 5 a) revealed a stronger association with the actual transfusion status for approach A (χ 2 = 829.07, p < 0.001; Cramér’s V = 0.44) than for B (χ 2 = 229.19, p < 0.001; Cramér’s V = 0.23). In terms of predictive metrics ( Table 6 a), approach A achieved higher overall accuracy (92.3%) with respect to B (87.5%), while B demonstrated better discrimination (AUC = 0.95 [95% CI: 0.94–0.96] vs. 0.85 [0.84–0.87]) and a lower Brier score (0.04 vs. 0.06). Table 5. Comparison between observational and model approaches in preoperative PBM eligibility. (a) Transfusion: PS24. Predicted by observational approach Predicted by model approach Preoperative PBM eligibility No Yes No Yes No 3185 35 2952 268 Yes 718 290 741 267 Total 3903 325 3693 535 Pearson chi2(1) 829.07 p < 0.001 229.19 p < 0.001 Cramér’s V 0.44 0.23 (b) Transfusion: PO. Predicted by observational approach Predicted by model approach Preoperative PBM eligibility No Yes No Yes No 3170 50 2947 273 Yes 670 338 717 291 Total 3840 388 3664 564 Pearson chi2(1) 941.93 p < 0.001 276.117 p < 0.001 Cramér’s V 0.47 0.26 Open in a new tab PS24: Early postoperative period: within 24 h after surgery. PO: Perioperative period: from 48 h before to 72 h after surgery. PBM: Patient blood management. Table 6. Evaluation parameters of the comparison between observational and model approaches. (a) Transfusion PS24. Accuracy Brier Score ROC Curve Area 95% Lower CI 95% Upper CI Observational approach 92.3% 0.06 0.85 0.84 0.87 Model approach 87.5% 0.04 0.95 0.94 0.96 (b) Transfusion: PO. Accuracy Brier Score ROC Curve Area 95% Lower CI 95% Upper CI Observational approach 90.8% 0.06 0.85 0.83 0.87 Model approach 86.6% 0.04 0.94 0.92 0.95 Open in a new tab PS24: Early postoperative period: within 24 h after surgery. PO: Perioperative period: from 48 h before to 72 h after surgery. Similar findings were observed for PO transfusions ( Table 5 b and Table 6 b). Although approach B provided a superior predictive precision (higher AUC, lower Brier score), approach A demonstrated better alignment with actual transfusion events, particularly in terms of classification accuracy and association measures. Given the low overall transfusion rate, additional classification metrics were calculated. For PS24 transfusions, the observational approach showed higher sensitivity (89.2%) compared to the multivariable model (49.9%), while specificity was comparable (81.6% vs. 79.9%). Both approaches demonstrated very high negative predictive values (>98% and almost 92%), reflecting the low event prevalence, whereas positive predictive values were modest (approximately 27 and 29%). Similar patterns were observed for PO transfusions (see Supplementary Table S4 ). 4. Discussion Despite its single-center retrospective design, this study draws on a large, unselected patient cohort and high-quality integrated institutional data to generate real-world evidence on the performance of PBM eligibility strategies. The analysis was intended to assess the accuracy of different risk stratification approaches in identifying patients at increased risk of perioperative transfusion in oncology surgery. The implementation of structured PBM programs has been consistently associated with a significant reduction in red blood cell transfusion requirements in patients undergoing oncological surgery [ 25 ]. As reduced transfusion exposure is linked to improved postoperative and long-term outcomes, an evidence-based and judicious approach to the identification and management of preoperative anemia remains essential [ 26 , 27 ]. Currently, PBM eligibility in major surgery is largely guided by preoperative hemoglobin levels and surgical complexity [ 28 ]. However, these criteria have not been specifically validated in oncological populations, where disease-related factors and procedural heterogeneity may substantially influence transfusion risk [ 29 , 30 ]. Moreover, given the time-sensitive nature of cancer surgery, preoperative anemia should not necessarily result in surgical delay; rather, it should prompt timely and targeted correction, through iron supplementation and optimization of erythropoiesis, as an integral component of routine perioperative care. In this study, we compared two approaches to defining PBM eligibility: an observational model, based on preoperative hemoglobin levels and estimated transfusion risk; and a multivariable modeling approach incorporating detailed preoperative, anesthetic and surgical variables. Both models were evaluated across two timeframes (early postoperative period PS24 and perioperative period PO) to capture the variable clinical impact of transfusions throughout the surgical course. Our findings demonstrated a strong and consistent association between the observational PBM eligibility criteria and actual perioperative transfusion risk in oncological surgery. High-risk surgical procedures and preoperative anemia emerged as independent and clinically meaningful predictors of transfusion. The disproportionately elevated transfusion rates observed in specific surgical categories, particularly sarcoma, gynecologic, and colorectal procedures, support a selective application of PBM interventions, enabling the focused allocation of resources to patients most likely to derive a benefit. Overall, only 23.8% of patients were identified as PBM candidates according to the observational model; however, this subgroup accounted for nearly 90% of all transfusions, confirming the model’s strong discriminatory capacity. The multivariable analysis further validated the association between transfusion risk and several independent predictors, including surgical complexity, operative duration, ICU admission risk, and anemia severity—with particularly high odds ratios for moderate and severe anemia. Comparative analysis revealed that, although the multivariable model achieved superior statistical discrimination, as reflected by higher AUC values, the observational approach demonstrated higher overall classification accuracy and stronger concordance with observed transfusion events (Cramér’s V = 0.44–0.47 vs. 0.23–0.25). These findings suggest that the observational PBM model represents a simple, pragmatic, and clinically reliable tool for identifying patients most likely to benefit from PBM interventions in complex oncological surgery. While the observational PBM eligibility criteria captured the majority of transfused patients, approximately 11% of transfusion events occurred in patients not classified as PBM-eligible. This subgroup represents a clinically relevant “gray area” for which standard hemoglobin-based and procedural criteria may underestimate risk. To better characterized this gray zone population, a prospective validation within a structured PBM framework is warranted to reduce the possible confounding role of non-corrected preoperative anemia in restricted transfusion practice. Additionally, to our knowledge, an international risk score is validated only for cardiological and traumatic surgery [ 31 ]. They identify about 25% of patients that could be classified as in the “gray area”, but we are not aware that such scores are also reliable in the oncological surgery setting. Nevertheless, prospective validation within a structured PBM framework is warranted. For individual patients, a multidisciplinary tumor board approach may facilitate the development of personalized PBM strategies focused on the (1) optimization of preoperative hemoglobin levels; (2) integration of multimodal blood-sparing techniques; and (3) adoption of rational, evidence-based transfusion practices. A coordinated, patient-centered strategy is likely essential to improve perioperative outcomes and, ultimately, the long-term prognosis in surgical oncology patients [ 15 ]. This study has some limitations: a retrospective single-center design, missing data and incomplete clinical documentation. Although the retrospective and single-center nature of this study may limit the external validity of the findings, the impact of this limitation appears mitigated by the large, consecutive, and unselected cohort analyzed. The consistency and magnitude of the observed associations across surgical categories and anemia strata, as well as the strong discriminatory performance of both PBM approaches, suggest that the main conclusions are robust and unlikely to be driven by center-specific artifacts. Missing data were present for some clinical variables; however, their proportion was generally low and mainly involved secondary parameters (e.g., hemodynamic measures). Importantly, key predictors of the transfusion risk (anemia status, surgical type, surgical complexity, and operative time) were almost complete. Therefore, while missingness represents a methodological limitation, it is unlikely to have materially biased the main results or altered the observed risk stratification patterns. Finally, incomplete documentation is an inherent limitation of real-world retrospective studies based on administrative and clinical databases. In this cohort, however, the availability of structured data from integrated institutional systems allowed for reliable capture of the main variables driving the transfusion risk. Given the very strong effect sizes observed in the multivariable models (e.g., for anemia severity and surgical complexity), residual information bias due to incomplete documentation is unlikely to substantially affect this study’s conclusions. 5. Conclusions This single-center retrospective study evaluated two distinct strategies for identifying oncological surgical patients at increased risk of perioperative blood transfusion, applying both approaches to the same real-world cohort. Standard PBM eligibility criteria, based on the preoperative anemia status and procedure-specific transfusion risk, successfully identified nearly 90% of transfused patients. Although a multivariable model incorporating detailed clinical and surgical variables achieved superior statistical discrimination, the observational PBM approach showed stronger alignment with actual transfusion practices and a higher classification accuracy. These findings suggest that routinely available PBM indicators provide a pragmatic and clinically meaningful tool for transfusion risk identification in complex oncological surgery, while the multivariable model primarily represents a benchmarking comparator for the observational PBM approach in order to make a future decision support tool. Given the retrospective design, causal inferences regarding the effectiveness of PBM interventions cannot be drawn. Nevertheless, the results support the use of structured PBM eligibility criteria as a foundation for transfusion risk stratification and resource prioritization. As practical guidance, PBM eligibility could be determined preoperatively through automated Electronic Medical Report (EMR)-based screening, which is not only feasible but also increasingly recommended as a part of modern perioperative optimization pathways. The implementation of preoperative anemia clinics as a core of PBM strategy and automated EMR flags can be used to identify PBM eligible patients preoperatively with digital perioperative pathways. Clinical resources could include a dedicated PBM team, standardized protocol for anemia work-up, clinical and informatics infrastructure, and training for clinicians and nurses to understand the alerts [ 32 ]. Prospective studies are warranted to evaluate how integrating observational and model-based approaches may improve PBM implementation and patient outcomes in oncological surgical care. Abbreviations The following abbreviations are used in this manuscript: PBM Patient blood management PS24 Transfusion events within 24 h after surgery PO Perioperative transfusion events OR Odds ratio INT Fondazione IRCCS Istituto Nazionale dei Tumori DWH Data WareHouse SIAARTI Italian Society of Anesthesia and Intensive Care NCCN National Comprehensive Cancer Network TR Transfusion rate TI Transfusion index STR Surgical transfusion risk Open in a new tab Supplementary Materials The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jcm15072543/s1 , Table S1: Classification of surgical complexity based on transfusion rate (TR) at INT at PS24 and transfusion index (TI) in the PO. Table S2: GLM covariates collinearity check by VIF. Table S3: Logistic multivariable analyses to test the comparability between cases excluded and included in the multivariable GLM model. Table S4: Scores of Sensitivity/Specificity for both approaches. jcm-15-02543-s001.zip (387.6KB, zip) Author Contributions Conceptualization: C.L., R.L. (Roberto Lillini), P.B. and G.P.; methodology: R.L. (Roberto Lillini), C.M.C. and P.V.; data curation: P.B., I.C., G.P., C.L. and R.L. (Rosamaria Limuti); formal analysis: R.L. (Roberto Lillini); interpretation: all authors; writing—original draft: C.L., R.L. (Roberto Lillini), P.B. and G.P.; writing—review and editing: all authors; supervision: F.A. and P.V. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Fondazione IRCCS Istituto Nazionale dei Tumori (protocol code INT 225/25, date: 17 December 2025). Informed Consent Statement Patient consent was waived due to disproportionate effort in contacting approximately 4300 eligible patients who had elective oncological surgeries and liver transplant procedures performed between 26 September 2023 and 30 June 2024, at the Fondazione IRCCS Istituto Nazionale dei Tumori (INT) in Milan. Moreover, in Italy, institutions like INT are known as IRCCS, which have a dual mandate to provide healthcare and conduct research. Under the Italian privacy law (Art 110-bis, c. 4, D.lgs. 196/03), IRCCS are permitted to use data collected for clinical purposes, which constitutes their real-world data, for monocentric research without needing explicit patient consent. This provision allows IRCCS to develop and utilize DWH for research activities. Data Availability Statement The datasets presented in this article are not readily available because of privacy and ethical restrictions. 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