Clinical impact of an EHR-integrated hyperkalemia management system: a real-world study of 10,934 episodes.

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Learn more: PMC Disclaimer | PMC Copyright Notice Ren Fail . 2026 Jun 2;48(1):2680349. doi: 10.1080/0886022X.2026.2680349 Clinical impact of an EHR-integrated hyperkalemia management system: a real-world study of 10,934 episodes Guangna Lyu a, b, c , Fang Zhang d , Xiuzhao Fan d , Rongshan Li a, c, e, f , Xiaoshuang Zhou a, c, d, e, f, g, ✉ Author information Article notes Copyright and License information a Department of Nephrology, Shanxi Provincial People’s Hospital, Taiyuan, China b The Second People’s Hospital of Shanxi Province, Taiyuan, China c Department of Nephrology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China d Big Data Center of Kidney Disease, Shanxi Provincial People’s Hospital, Taiyuan, China e Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China f Medicinal Basic Research Innovation Center of Chronic Kidney Disease, Ministry of Education, Shanxi Medical University, Taiyuan, China g CollegeIII of Clinical Medicine, Shanxi University of Traditional Chinese Medicine, Taiyuan, China ✉ CONTACT Xiaoshuang Zhou xiaoshuangzhou69@163.com The Nephrology Department of Shanxi Provincial People’s Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, China. Roles Guangna Lyu : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing Fang Zhang : Data curation, Formal analysis, Software, Visualization Xiuzhao Fan : Data curation, Formal analysis, Software, Visualization Rongshan Li : Conceptualization, Supervision, Writing – review & editing Xiaoshuang Zhou : Conceptualization, Formal analysis, Project administration, Supervision, Writing – review & editing Received 2026 Jan 30; Accepted 2026 May 22; Collection date 2026. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/ ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. PMC Copyright notice PMCID: PMC13235244  PMID: 42231677 Abstract This retrospective study analyzed 10,934 hyperkalemia episodes to compare outcomes between the hyperkalemia routine group and the hyperkalemia standardized group, the latter managed with a strategy supported by the ‘Serum Potassium Management Center System’ in a real-world implementation setting. The study compared process and outcome metrics between this system-level intervention cohort and the routine care cohort. The hyperkalemia standardized group exhibited significantly higher rates of diagnosis (19.00% vs. 15.43%), treatment (85.23% vs. 58.29%), and review (89.41% vs. 60.86%) (all p < 0.05). With respect to disease burden, the hyperkalemia standardized group was associated with lower hospitalization costs in the chronic kidney disease-heart failure (CKD-HF) and chronic kidney disease-diabtes mellitus-heart failure comorbid subgroups, as well as shorter hospital stays in the CKD-HF comorbid subgroup (all p < 0.05). A non-significant trend toward reduced hospitalization costs and shorter hospital stays was also observed in the CKD subgroup. In conclusion, implementation of the ‘Serum Potassium Management Center System’ was associated with improved quality of hyperkalemia care, as reflected by more favorable process indicators and disease burden-related outcomes. This trial was registered with the Chinese Clinical Trial Registry (ChiCTR) ChiCTR2600116550 on January 12, 2026. Keywords: Hyperkalemia, standardized management, diagnosis rate, treatment rate, review rate, disease burden 1. Introduction Hyperkalemia is a common electrolyte disorder associated with substantial clinical risks, including life‑threatening arrhythmias and even cardiac arrest in severe cases [ 1 , 2 ]. Accumulating evidence has linked hyperkalemia to prolonged hospitalization, increased all-cause mortality, and considerable economic burdens [ 3–6 ]. This electrolyte disturbance is particularly prevalent in patients with chronic kidney disease (CKD), heart failure (HF), and diabetes mellitus (DM) [ 7 ]. Moreover, the coexistence of hyperkalemia with these comorbidities further elevates mortality risk and exacerbates economic consequences [ 4 , 8 ]. Accordingly, early identification, diagnosis, and timely intervention for hyperkalemia remain critically important in clinical practice. International guidelines, including the 2018 European Society of Cardiology (ESC) guidelines and the 2020 Kidney Disease Improving Global Outcomes (KDIGO) Expert Consensus on Potassium Management in Kidney Disease, define hyperkalemia as a serum potassium level exceeding 5.0 mmol/L [ 9 , 10 ]. Standardization of the hyperkalemia definition helps enhance clinician awareness, enable timely identification and targeted intervention, and reduce the associated adverse clinical risks. The 2018 ESC guidelines further categorize hyperkalemia into three grades of severity: (1) mild: 5.0–5.5 mmol/L; (2) moderate: 5.5–6.0 mmol/L; (3) severe: > 6.0 mmol/L [ 9 ]. This severity stratification facilitates individualized treatment decisions and risk assessment according to severity. The primary therapeutic goal is to rapidly and safely restore serum potassium levels to the normal range. Despite its substantial clinical impact, research on standardized management protocols for hyperkalemia remains limited. This study introduces a novel management paradigm by comparing the diagnosis rate, treatment effectiveness, follow-up compliance, and disease burden before and after the implementation of the ‘Serum Potassium Management Center System’ at our institution. The aim is to establish an integrated approach encompassing diagnosis, treatment, and follow-up to minimize adverse events and reduce the clinical and economic burdens associated with hyperkalemia. Ultimately, this integrated strategy is expected to improve clinical outcomes and quality of life for patients with hyperkalemia. This study evaluates a clinical decision support system (CDSS) designed as a real-time alert system with full Electronic Health Record (EHR) integration, which combines automated detection, alerting, and structured follow-up for hyperkalemia. Representing an application of digital health technology in routine care, this work extends beyond prior pharmacologic-focused investigations by examining system-level intervention effects on care processes and resource utilization. 2. Materials and methods 2.1. Data source Data were collected via the clinical research platform of Shanxi Provincial People’s Hospital and the ‘Serum Potassium Management Center System’, which was developed collaboratively by the Department of Nephrology and the Information Management Center of our hospital. We retrieved all inpatients who had undergone at least one serum potassium measurement at Shanxi Provincial People’s Hospital. Patients meeting the inclusion criteria were identified, and their clinical information was extracted by linking the Electronic Medical Record System (EMRS), Laboratory Information System (LIS), and physician order records to ascertain the utilization of potassium-lowering therapies. This system integrates data from the Hospital Information System (HIS), LIS, and EMRS to construct a comprehensive database. It can receive and display serum potassium results within 2 min of LIS reporting, with a complete record of all previous potassium measurements presented on the same interface. The system supports targeted screening by time period, potassium range (hyperkalemia: >5.0 mmol/L; normal: 3.5–5.0 mmol/L; hypokalemia: <3.5 mmol/L), inpatient/outpatient status, department, and basic patient characteristics. An automatic alert is activated when serum potassium is < 3.5 mmol/L or > 5.0 mmol/L. These are interruptive pop-up notifications sent directly to the attending physician within the EMRS, and clinicians must acknowledge the alert before proceeding with other tasks. Treatment recommendations are generated automatically based on the severity of hyperkalemia or hypokalemia. Additionally, dedicated serum potassium administrators can provide personalized management instructions upon identifying abnormal potassium levels, facilitating timely and precise intervention by the treating physician. Notably, no additional educational interventions, audit-feedback programs, or staffing adjustments were implemented during the study period. The study was approved by the Institutional Review Board of Shanxi Provincial People’s Hospital, Taiyuan, China [(2025) Provincial Medical Ethics Review No.907]. Due to the retrospective nature of this study, the requirement for informed consent was waived. Patient data were rigorously de-identified prior to analysis to ensure confidentiality and privacy. The study was conducted in accordance with the principles of the Declaration of Helsinki. 2.2. Data collection Inclusion criteria were as follows: (1) age ≥ 18 years, and (2) at least one serum potassium level > 5.0 mmol/L during hospitalization. Patients were excluded if they had pseudohyperkalemia (e.g., specimen hemolysis, marked leukocytosis, or thrombocytosis) [ 11 ], malignant tumors, a life expectancy < 3 months, transfer to another facility during initial treatment, active infectious diseases, or a hospital stay > 90 days. Clinical characteristics were collected separately for the hyperkalemia standardized group and the hyperkalemia routine group, including age, sex, height, weight, body mass index (BMI), use of renin-angiotensin-aldosterone system inhibitors (RAASi) or mineralocorticoid receptor antagonists (MRA), disease duration, and laboratory parameters. Laboratory and clinical outcomes included admission serum creatinine, estimated glomerular filtration rate (eGFR), post-admission serum potassium levels, hyperkalemia documentation, administration of potassium-lowering therapy within 24 h, repeat serum potassium testing within 7 days, hospital stays, and hospitalization costs. A total of 10,934 patients were enrolled in this study. Among them, 8,355 were assigned to the hyperkalemia routine group before the implementation of the ‘Serum Potassium Management Center System’, and 2,579 to the hyperkalemia standardized group after system implementation. Four subgroups were identified in both the standardized and routine hyperkalemia management groups. In the standardized group, there were 86 patients with CKD, 61 with comorbid CKD and HF, 56 with comorbid CKD and DM, and 68 with comorbid CKD, HF, and DM. In the routine group, there were 155 patients with CKD, 111 with comorbid CKD and HF, 127 with comorbid CKD and DM, and 123 with comorbid CKD, HF, and DM. To ensure that the study cohort reflected real-world clinical practice, patients with multiple comorbidities were assigned to the corresponding disease subgroups accordingly. Patients with only a single episode of reduced eGFR, lacking prior data and not meeting the diagnostic criteria for CKD, were excluded from the CKD subgroup. Owing to the small sample size, statistical analyses were not performed for the HF alone and DM alone subgroups. 2.3. Research indicators and definitions We compared the diagnosis rate, treatment rate, and review rate of hyperkalemia between the two groups before and after the implementation of standardized management. The hospital stays and hospitalization costs were also compared between the two groups among the following subgroups: hyperkalemia with CKD, comorbid CKD and HF, comorbid CKD and DM, and comorbid CKD, HF, and DM. Hyperkalemia was defined as at least one serum potassium level >5.0 mmol/L. The hyperkalemia diagnosis rate was defined as the proportion of patients with hyperkalemia who received a documented diagnosis of hyperkalemia (including ‘high serum potassium’ or ‘hyperpotassemia’) in the medical record. The hyperkalemia treatment rate was defined as the proportion of hyperkalemic patients who received potassium-lowering therapy within 24 h after hyperkalemia identification. Potassium-lowering therapy was defined as administration of any of the following agents or modalities: sodium zirconium cyclosilicate, sodium polystyrene sulfonate, calcium polystyrene sulfonate, glucose plus insulin, sodium bicarbonate injection, furosemide injection or tablets, torsemide injection or tablets, hydrochlorothiazide tablets, salbutamol, terbutaline, hemodialysis, peritoneal dialysis, or hemofiltration. Among renal replacement therapies, hemodialysis and hemofiltration were mainly primarily utilized for the acute management of hyperkalemia, whereas peritoneal dialysis was predominantly administered as chronic maintenance therapy rather than acute potassium-lowering treatment. The post-treatment serum potassium level was lower than the pretreatment level. The hyperkalemia review rate was defined as the proportion of hyperkalemic patients who underwent repeat serum potassium testing within 7 days after hyperkalemia identification. The eGFR was calculated using the 2009 KDIGO creatinine‑based equation for estimated glomerular filtration rate [ 12 ]. 2.4. Statistical analysis Analyses were performed using SPSS 23.0. The Shapiro-Wilk test was used to evaluate the normality of quantitative data. Due to the non-normal distribution, continuous variables were expressed as the medians and interquartile ranges [M(QL, QU)] and compared utilizing the Mann–Whitney U-test. Categorical variables were presented as case numbers and frequencies (%) and analyzed using the Chi-square test. A two-tailed p < 0.05 was considered statistically significant. To evaluate the independent association between implementation of the ‘Serum Potassium Management Center System’ and hospitalization outcomes (prolonged hospital stays and elevated hospitalization costs), binary logistic regression analyses were performed. Variables for inclusion in the multivariate models were selected a priori based on clinical relevance and existing literature. The models were adjusted for these predefined potential confounders. Results are reported as adjusted odds ratios (OR) with 95% confidence intervals (95%CI), with statistical significance maintained at p < 0.05 (two-tailed). 3. Results 3.1. Basic Information and rates A total of 10,934 patients were enrolled in the study, comprising 8,355 in the hyperkalemia routine group and 2,579 in the hyperkalemia standardized group. Among these, 490 patients in the standardized group and 1,289 in the routine group received a diagnosis of hyperkalemia. Additionally, 2,198 patients in the standardized group and 4,870 in the routine group received potassium-lowering therapy within 24 h, while 2,306 patients in the standardized group and 5,085 in the routine group underwent repeat serum potassium testing within 7 days following the initial hyperkalemic episode. Mild hyperkalemia was the most prevalent severity category in both groups. The hyperkalemia standardized group demonstrated significantly higher rates of diagnosis, treatment, and review compared to the hyperkalemia routine group (all p < 0.05). Specifically, diagnosis rates for mild, moderate, and severe hyperkalemia were all higher in the standardized group, with statistically significant differences observed for mild and moderate hyperkalemia ( p < 0.05). Similarly, the treatment and review rates for hyperkalemia were significantly improved in the standardized group relative to the routine group ( p < 0.05) ( Tables 1 and 2 ). Across all serum potassium levels, the diagnosis rate was lower than the treatment rate, and the treatment rate was lower than the review rate in both groups. Furthermore, the diagnosis, treatment, and review rates for hyperkalemia in both groups increased with increasing serum potassium levels. Table 1. Basic information and comparison of rates of the study cases. Variable hyperkalemia standardized group ( n = 2,579) hyperkalemia routine group ( n = 8,355) p -value Mild hyperkalemia 1,160 (44.98%) 3,694 (44.21%) 0.494 Moderate hyperkalemia 802 (31.10%) 2,507 (30.00%) 0.292 Severe hyperkalemia 617 (23.92%) 2,154 (25.78%) 0.058 Open in a new tab Data are given as n (%). Table 2. Basic information and comparison of rates of the study cases. Variable hyperkalemia standardized group ( n = 2,579) hyperkalemia routine group ( n = 8,355) OR (95%CI) p -value Diagnosis of hyperkalemia mild hyperkalemia 140 (12.07%) 192 (5.20%) 2.503 (1.992-3.147) <0.001 moderate hyperkalemia 121 (15.09%) 306 (12.21%) 1.278 (1.018-1.604) 0.034 severe hyperkalemia 229 (37.12%) 791 (36.72%) 1.017 (0.845-1.224) 0.858 Total 490 (19.00%) 1,289 (15.43%) 1.286 (1.146-1.442) <0.001 Treatment of hyperkalemia mild hyperkalemia 878 (75.69%) 806 (21.82%) 11.156 (9.552-13.029) <0.001 moderate hyperkalemia 722 (90.02%) 2,018 (80.49%) 2.187 (1.701-2.811) <0.001 severe hyperkalemia 598 (96.92%) 2,046 (94.99%) 1.661 (1.012-2.728) 0.043 Total 2,198 (85.23%) 4,870 (58.29%) 4.128 (3.672-4.641) <0.001 Review of hyperkalemia mild hyperkalemia 971 (83.71%) 960 (25.99%) 14.631 (12.316-17.383) <0.001 moderate hyperkalemia 732 (91.27%) 2,056 (82.01%) 2.294 (1.759-2.992) <0.001 severe hyperkalemia 603 (97.73%) 2,069 (96.05%) 1.769 (0.998-3.137) 0.048 Total 2,306 (89.41%) 5,085 (60.86%) 5.432 (4.756-6.204) <0.001 Open in a new tab Data are given as n (%) or OR (95%CI). 3.2. Baseline characteristics, hospital stays, and hospitalization expenses 3.2.1. CKD subgroup analysis The CKD subgroup comprised 86 patients in the hyperkalemia standardized group and 155 patients in the hyperkalemia routine group. Baseline characteristics, including sex, age, BMI, eGFR, disease duration, use of RAASi/MRA, serum potassium levels, and hyperkalemia severity grade, were compared between the two groups. No significant between-group differences were observed in any of these baseline variables ( p > 0.05) ( Table 3 ). After balancing for baseline characteristics, the hospital stays in the CKD subgroup were numerically shorter in the hyperkalemia standardized group than in the routine group, though the difference did not reach statistical significance ( p > 0.05). Similarly, hospitalization costs in the CKD subgroup were lower in the standardized group compared to the routine group, without achieving statistical significance ( p > 0.05) ( Table 7 ). In conclusion, the implementation of the ‘Serum Potassium Management Center System’ was not associated with significant improvements in hospital stays or hospitalization costs among patients in the CKD subgroup. Table 3. Baseline characteristics of the CKD subgroup. Patient characteristics hyperkalemia standardized group ( n = 86) hyperkalemia routine group ( n = 155) p -value Sex, n (%) 0.534 Female 30 (34.88%) 48 (30.97%) Male 56 (65.12%) 107 (69.03%) Age, years 64.00 (46.75, 72.00) 60.00 (42.00, 72.00) 0.229 BMI, kg/m² 21.80 (19.38, 23.90) 22.50 (21.00, 24.20) 0.076 eGFR, mL/min/1.73m² 9.27 (5.56, 30.23) 9.90 (5.38, 19.47) 0.309 Duration of disease, months 12.50 (3.75, 54.00) 15.00 (4.00, 60.00) 0.448 RAASi/MRA use, n (%) 38 (44.19%) 78 (50.32%) 0.361 Serum potassium, mmol/L 5.55 (5.29, 6.09) 5.62 (5.30, 6.10) 0.489 Grade of hyperkalemia, n (%) 0.695 Mild 42 (48.84%) 68 (43.87%) Moderate 19 (22.09%) 41 (26.45%) Severe 25 (29.07%) 46 (29.68%) Open in a new tab Data are given as M (QL, QU) or n (%). CKD, chronic kidney disease; HF, heart failure; DM, diabetes mellitus; BMI, body mass index; eGFR, estimated glomerular filtration rate; RAASi, renin–angiotensin–aldosterone system inhibitors; MRA, mineralocorticoid receptor antagonists. Table 7. The hospital stays and hospitalization costs in the CKD, CKD-HF comorbid, CKD-DM comorbid, and CKD-HF-DM comorbid subgroups. Group Subgroup Hyperkalemia standardized group Hyperkalemia Routine Group p -value CKD Hospital stay, days 8.00 (5.00,14.25) 9.00 (6.00,14.00) 0.718 Hospitalization costs, RMB 13,728.87 (8,601.64, 26,277.90) 14,981.60 (10,181.80, 31,981.60) 0.313 CKD-HF comorbid Hospital stay, days 9.00 (6.00,13.50) 13.00 (8.00,18.00) 0.002 Hospitalization costs, RMB 19,075.79 (10,008.72,30,313.29) 24,023.40 (14,388.80,41,647.40) 0.012 CKD-DM comorbid Hospital stay, days 10.00 (6.00,14.00) 10.00 (5.00,15.00) 0.850 Hospitalization costs, RMB 16,198.12 (8,512.97,32588.33) 16,170.40 (9,565.70,26,246.60) 0.839 CKD-HF-DM comorbid Hospital stay, days 10.00 (7.00,12.75) 10.00 (6.00,16.00) 0.368 Hospitalization costs, RMB 18,278.02 (12,066.93,29,244.77.81) 24,811.04 (14,062.40,39,822.40) 0.028 Open in a new tab Data are given as M (QL, QU). CKD, chronic kidney disease; HF, heart failure; DM, diabetes mellitus. 3.2.2. CKD-HF comorbid subgroup analysis The CKD-HF comorbid subgroup comprised 61 patients in the hyperkalemia standardized group and 111 patients in the hyperkalemia routine group. Baseline characteristics, including sex, age, BMI, eGFR, disease duration, use of RAASi/MRA, serum potassium levels, and hyperkalemia severity grade, were compared between the two groups. No significant between-group differences were observed in any of these baseline variables ( p > 0.05) ( Table 4 ). Following adjustment for baseline characteristics, the CKD-HF comorbid subgroup in the hyperkalemia standardized group exhibited significantly shorter hospital stays and lower hospitalization costs compared with the routine group ( p < 0.05) ( Table 7 ). In conclusion, implementation of the ‘Serum Potassium Management Center System’ was associated with significantly shorter hospital stays and lower hospitalization costs in the CKD-HF comorbid subgroup. Table 4. Baseline characteristics of the CKD-HF comorbid subgroup. Patient characteristics hyperkalemia standardized group ( n = 61) hyperkalemia routine group ( n = 111) p -value Sex, n (%) 0.194 Female 27 (44.26%) 38 (34.23%) Male 34 (55.74%) 73 (65.77%) Age, years 60.00 (43.00, 76.00) 66.00 (49.00, 78.00) 0.163 BMI, kg/m² 21.90 (19.45, 25.15) 22.00 (20.50, 23.30) 0.977 eGFR, mL/min/1.73m² 6.83 (4.98, 17.34) 7.27 (5.17, 17.28) 0.960 Duration of disease, months 17.00 (8.00,67.00) 47.00 (12.00, 96.00) 0.197 RAASi/MRA use, n (%) 30 (49.18%) 47 (42.34%) 0.388 Serum potassium, mmol/L 5.76 (5.30, 6.04) 5.61 (5.28, 6.26) 0.934 Grade of hyperkalemia, n (%) 0.570 Mild 24 (39.34%) 48 (43.24%) Moderate 20 (32.79%) 28 (25.23%) Severe 17 (27.87%) 35 (31.53%) Open in a new tab Data are given as M (QL, QU) or n (%). CKD, chronic kidney disease; HF, heart failure; DM, diabetes mellitus; BMI, body mass index; eGFR, estimated glomerular filtration rate; RAASi, renin–angiotensin–aldosterone system inhibitors; MRA, mineralocorticoid receptor antagonists. 3.2.3. CKD-DM comorbid subgroup analysis The CKD-DM comorbid subgroup comprised 56 patients in the hyperkalemia standardized group and 127 patients in the hyperkalemia routine group. Baseline characteristics, including sex, age, BMI, eGFR, disease duration, use of RAASi/MRA, serum potassium levels, and hyperkalemia severity grade, were compared between the two groups. No significant between-group differences were observed in any of these baseline variables ( p > 0.05) ( Table 5 ). Following adjustment for baseline characteristics, the hospital stays, and hospitalization costs in the CKD-DM comorbid subgroup did not demonstrate significant reductions in the hyperkalemia standardized group compared with the routine group ( p > 0.05) ( Table 7 ). In conclusion, implementation of the ‘Serum Potassium Management Center System’ was not associated with significant improvements in hospital stays or hospitalization costs in the CKD-DM comorbid subgroup. Table 5. Baseline characteristics of the CKD-DM comorbid subgroup. Patient characteristics hyperkalemia standardized group ( n = 56) hyperkalemia routine group ( n = 127) p -value Sex, n (%) 0.383 Female 25 (44.64%) 48 (37.80%) Male 31 (55.36%) 79 (62.20%) Age, years 63.50 (57.25, 73.75) 62.00 (55.00, 71.00) 0.236 BMI, kg/m² 23.70 (21.63, 25.50) 23.00 (21.00, 25.50) 0.262 eGFR, mL/min/1.73m² 12.84 (6.13, 25.75) 9.12 (5.62, 16.44) 0.120 Duration of disease, months 180.00 (99.00,240.00) 168.00 (96.00,240.00) 0.667 RAASi/MRA use, n (%) 37 (66.07%) 76 (59.84%) 0.424 Serum potassium, mmol/L 5.68 (5.41, 6.11) 5.66 (5.23, 6.14) 0.652 Grade of hyperkalemia, n (%) 0.376 Mild 15 (26.79%) 47 (37.01%) Moderate 24 (42.86%) 44 (34.65%) Severe 17 (30.36%) 36 (28.35%) Open in a new tab Data are given as M (QL, QU) or n (%). CKD, chronic kidney disease; HF, heart failure; DM, diabetes mellitus; BMI, body mass index; eGFR, estimated glomerular filtration rate; RAASi, renin–angiotensin–aldosterone system inhibitors; MRA, mineralocorticoid receptor antagonists. 3.2.4. CKD-DM-HF comorbid subgroup analysis The CKD-DM-HF comorbid subgroup comprised 68 patients in the hyperkalemia standardized group and 123 patients in the hyperkalemia routine group. Baseline characteristics, including sex, age, BMI, eGFR, disease duration, use of RAASi/MRA, serum potassium levels, and hyperkalemia severity grade, were compared between the two groups. No significant between-group differences were observed in any of these baseline variables ( p > 0.05) ( Table 6 ). Following adjustment for baseline characteristics, the hospital stays in the CKD-DM-HF comorbid subgroup did not differ significantly between the hyperkalemia standardized group and the routine group ( p > 0.05). However, hospitalization costs in this subgroup were significantly lower in the standardized group compared to the routine group ( p < 0.05) ( Table 7 ). In summary, the implementation of the ‘Serum Potassium Management Center System’ was not associated with a significant reduction in hospital stays, but was associated with decreased hospitalization costs in the CKD-DM-HF comorbid subgroup. Table 6. Baseline characteristics of the CKD-DM-HF comorbid subgroup. Patient characteristics hyperkalemia standardized group ( n = 68) hyperkalemia routine group ( n = 123) p -value Sex, n (%) 0.281 Female 32 (47.06%) 48 (39.02%) Male 36 (52.94%) 75 (60.98%) Age, years 62.50 (54.25, 67.00) 65.00 (56.00, 77.00) 0.066 BMI, kg/m² 23.70 (21.23, 25.60) 22.90 (20.50, 25.70) 0.203 eGFR, mL/min/1.73m² 9.94 (6.19, 17.90) 10.49 (6.16, 17.45) 0.718 Duration of disease, months 192.00 (120.00,240.00) 192.00 (108.00,240.00) 0.648 RAASi/MRA use, n (%) 52 (76.47%) 83 (67.48%) 0.191 Serum potassium, mmol/L 5.74 (5.37, 6.12) 5.60 (5.27, 6.15) 0.542 Grade of hyperkalemia, n (%) 0.330 Mild 21 (30.88%) 50 (40.65%) Moderate 26 (38.24%) 36 (29.27%) Severe 21 (30.88%) 37 (30.08%) Open in a new tab Data are given as M (QL, QU) or n (%). CKD, chronic kidney disease; HF, heart failure; DM, diabetes mellitus; BMI, body mass index; eGFR, estimated glomerular filtration rate; RAASi, renin–angiotensin–aldosterone system inhibitors; MRA, mineralocorticoid receptor antagonists. 3.3. Binary logistic regression analyses Binary logistic regression models were employed to identify factors associated with the hospitalization outcomes examined in this study, specifically hospital stays and hospitalization costs. The threshold values for prolonged hospital stays and elevated hospitalization costs were elevated based on the median values across all subgroups. Values greater than or equal to the respective median were classified as prolonged hospital stays or high hospitalization costs, whereas values below the median were categorized as short hospital stays or low hospitalization costs. Patients aged ≥ 60 years were assigned to the elderly group, while those aged < 60 years were assigned to the non-elderly group. To examine the factors influencing hospital stays and hospitalization costs, we constructed separate regression models. The independent variables included gender, age, BMI, eGFR, RAASi/MRA use, disease duration, serum potassium, 30-day readmission, comorbid conditions, and adoption of the ‘Serum Potassium Management Center System. Additionally, when analyzing hospitalization costs as the dependent variable, hospital stays were included as an independent variable. To ensure model validity, we performed the Hosmer-Lemeshow test, which confirmed satisfactory goodness-of-fit for all binary logistic regression models ( p > 0.05 for all tests). Multicollinearity diagnostics further confirmed model robustness, with all tolerance values exceeding 0.2 and variance inflation factors (VIF) remaining below 5, suggesting no significant multicollinearity among predictors. The detailed regression results are presented in Tables 8 and 9 . Table 8. Multivariate binary logistic regression analysis of factors associated with hospital stays. Variables β Std. Error Wald χ 2 p -value OR 95%CI Sex (ref: Male) Female 0.202 0.154 1.724 0.189 1.224 0.905 1.653 Age (ref: non-elderly) −0.269 0.160 2.827 0.093 0.764 0.558 1.046 BMI 0.030 0.023 1.777 0.182 1.031 0.986 1.078 eGFR −0.003 0.005 0.406 0.524 0.997 0.988 1.006 Duration of disease 0.000 0.001 0.054 0.816 1.000 0.998 1.002 RAASi/MRA use (ref: No RAASi/MRA use) −0.031 0.153 0.042 0.838 0.969 0.718 1.309 Serum potassium −0.345 0.121 8.165 0.004 0.708 0.559 0.897 30-day readmission (ref: No 30‑day readmission) −0.174 0.200 0.758 0.384 0.841 0.569 1.243 Co-morbid conditions (ref: CKD) 8.743 0.033 CKD-HF comorbidity 0.560 0.207 7.329 0.007 1.751 1.167 2.626 CKD-DM comorbidity 0.322 0.230 1.964 0.161 1.380 0.880 2.165 CKD-DM-HF comorbidity 0.495 0.234 4.487 0.034 1.641 1.038 2.594 The ‘Serum Potassium Management Center System’ (ref: No system use) −0.314 0.156 4.069 0.044 0.730 0.538 0.991 Constant 1.393 0.923 2.279 0.131 4.028 Open in a new tab CKD, chronic kidney disease; HF, heart failure; DM, diabetes mellitus; BMI, body mass index; eGFR, estimated glomerular filtration rate; RAASi, renin–angiotensin–aldosterone system inhibitors; MRA, mineralocorticoid receptor antagonists. Table 9. Multivariate binary logistic regression analysis of factors associated with hospitalization costs. Variables β Std. Error Wald χ 2 p -value OR 95%CI Sex (ref: Male) Female 0.065 0.177 0.134 0.714 1.067 0.754 1.510 Age (ref: non-elderly) 0.040 0.186 0.045 0.832 1.040 0.722 1.498 BMI −0.039 0.026 2.211 0.137 0.961 0.913 1.013 eGFR −0.005 0.005 1.044 0.307 0.995 0.984 1.005 Duration of disease 0.001 0.001 0.820 0.365 1.001 0.999 1.003 RAASi/MRA use (ref: No RAASi/MRA use) 0.020 0.178 0.012 0.912 1.020 0.720 1.445 Serum potassium 0.091 0.140 0.424 0.515 1.095 0.833 1.440 30-day readmission (ref: No 30‑day readmission) −0.364 0.233 2.439 0.118 0.695 0.440 1.097 comorbid conditions (ref: CKD) 13.983 0.003 CKD-HF comorbidity 0.464 0.239 3.773 0.052 1.591 0.996 2.542 CKD-DM comorbidity −0.316 0.268 1.391 0.238 0.729 0.431 1.233 CKD-DM-HF comorbidity 0.451 0.271 2.780 0.095 1.571 0.924 2.670 The ‘Serum Potassium Management Center System’ (ref: No system use) −0.191 0.180 1.133 0.287 0.826 0.581 1.175 hospital stays 2.280 0.174 172.449 0.000 9.779 6.958 13.744 Constant −0.922 1.073 0.738 0.390 0.398 Open in a new tab CKD, chronic kidney disease; HF, heart failure; DM, diabetes mellitus; BMI, body mass index; eGFR, estimated glomerular filtration rate; RAASi, renin–angiotensin–aldosterone system inhibitors; MRA, mineralocorticoid receptor antagonists. As shown in Table 8 , several factors were independently associated with prolonged hospital stays. A statistically significant overall effect was observed for comorbidities ( p < 0.05). Notably, patients with CKD-HF comorbidity and those with CKD-DM-HF comorbidity demonstrated significantly higher risks of extended hospital stays. Implementation of the ‘Serum Potassium Management Center System’ was associated with a substantial 27.3% reduction in the risk of prolonged hospital stays. Furthermore, each 1-unit increment in serum potassium concentration was independently associated with a 29.4% decreased likelihood of prolonged hospital stays. No significant associations were found between prolonged hospital stays and other examined variables, including age, BMI, eGFR, RAASi/MRA use, disease duration, or 30-day readmission rates (all p > 0.05). As shown in Table 9 , multivariate regression analysis identified key factors independently associated with increased hospitalization costs. Notably, prolonged hospital stay emerged as a significant predictor of higher costs ( p < 0.05). Comorbidities collectively demonstrated a substantial impact ( p = 0.003), with CKD-HF comorbidity showing a trend toward increased cost burden ( p = 0.052). In contrast, other variables – including age, BMI, eGFR, RAASi/MRA use, disease duration, 30-day readmission, and implementation of the ‘Serum Potassium Management Center System’ – were not independently associated with elevated hospitalization costs (all p > 0.05). 4. Discussion In this retrospective study, we investigated a novel management model for hyperkalemia by comparing the diagnosis rate, treatment rate, review rate, hospital stays, and hospitalization costs before and after implementation of the standardized management strategy. The findings of this study should be interpreted within the broader context of current guideline-directed management and existing pharmacologic strategies for hyperkalemia. International guidelines, including those from KDIGO and the ESC, emphasize the importance of regular electrolyte monitoring, judicious use of RAASi, and timely initiation of potassium binders in at-risk populations [ 9 , 10 ]. Similarly, clinical trials have established the efficacy of novel oral potassium binders and optimized RAASi dosing protocols in achieving long-term potassium control. However, these pharmacologic approaches primarily address the biologic mechanism of potassium handling once hyperkalemia is detected or anticipated. This system addresses gaps in care delivery rather than pharmacologic efficacy. This system represents a rule-based clinical decision support framework embedded within routine care. By automating detection, standardizing alert workflows, and enforcing structured follow-up, the ‘Serum Potassium Management Center System’ targets the operational failures – delayed recognition, inconsistent management, and loss to follow-up – that persist even when effective therapies exist. Consequently, the improvements in process indicators observed here are complementary to, but distinct from, the therapeutic gains achievable through pharmacologic optimization alone. Previous studies have demonstrated that rapidly rising serum potassium concentrations, prolonged hyperkalemia, and delayed follow-up monitoring are associated with an increased risk of adverse clinical outcomes in patients with hyperkalemia [ 13–16 ]. For example, among critically ill patients, a reduction in serum potassium of 1.0 mmol/L within 48 h has been shown to significantly decrease mortality risk [ 17 ]. However, hyperkalemia often has an insidious onset and nonspecific clinical manifestations, resulting in suboptimal rates of diagnosis, treatment, and follow‑up [ 13 , 18 , 19 ]. Even when hyperkalemia is identified, clinicians may not prioritize timely intervention, which may expose patients to an increased risk of adverse events. This study revealed that the implementation of the ‘Serum Potassium Management Center System’ was associated with significant improvements in the diagnosis, treatment, and review rates of hyperkalemia, with statistically significant differences. These findings underscore the potential value of standardized management in mitigating risks associated with hyperkalemia. Specifically, the diagnosis rates for mild, moderate, and severe hyperkalemia all increased following the system’s implementation, with statistically significant improvements observed for mild and moderate cases. This suggests that standardized management may enhance clinicians’ awareness and vigilance regarding mild to moderate hyperkalemia. Meanwhile, the lack of a statistically significant difference in the diagnosis rate for severe hyperkalemia may reflect clinicians’ already heightened attention to this critical condition. Timely diagnosis of hyperkalemia facilitates prompt treatment and enables regular serum potassium monitoring. Following the implementation of the ‘Serum Potassium Management Center System’, the treatment and review rates for mild, moderate, and severe hyperkalemia demonstrated significant improvements, further substantiating the efficacy of this management approach. Additionally, the study revealed that, irrespective of the system’s application, the diagnosis, treatment, and review rates of hyperkalemia increased with higher serum potassium levels, consistent with previous research [ 19 , 20 ]. Notably, the diagnosis rate remained lower than the treatment rate across all serum potassium levels, aligning with earlier studies [ 19 ]. In clinical practice, clinicians often initiate appropriate management for hyperkalemia promptly upon identifying elevated serum potassium levels, which may be prompted by laboratory alerts, clinical protocols, or automated system notifications. In many instances, timely treatment is initiated before formal diagnostic coding is completed in the electronic medical record. This discrepancy between early clinical intervention and subsequent diagnostic documentation best explains why the treatment rate appeared higher than the diagnosis rate. Hyperkalemia is associated with prolonged hospital stays, higher medical costs, and increased healthcare resource utilization compared to normokalemic patients, imposing a substantial clinical and economic burden that has been extensively documented across diverse countries and populations [ 21–26 ]. This considerable burden underscores the urgent need for optimized hyperkalemia management strategies, particularly among high-risk patient groups. Patients with CKD face an elevated risk of hyperkalemia due to reduced glomerular filtration rate, impaired urinary potassium excretion, and metabolic acidosis [ 27 , 28 ]. Similarly, patients with DM are predisposed to hyperkalemia secondary to impaired potassium excretion, tubular dysfunction, and attenuated intracellular potassium shift [ 29 ]. Patients with HF also carry a heightened hyperkalemia risk as a result of decreased renal perfusion [ 30 ]. Furthermore, RAASi and MRA – cornerstone therapies for CKD, DM, and HF – further exacerbate the risk of incident hyperkalemia [ 31–34 ]. Consequently, the prevalence of hyperkalemia is markedly higher among patients with these chronic conditions, particularly those with multiple concurrent comorbidities. In the present study, we conducted subgroup analyses among hyperkalemic patients with CKD, CKD-HF, CKD-DM, and CKD-DM-HF to assess variations in hospital length of stay and hospitalization costs before and after the implementation of the ‘Serum Potassium Management Center System’. Our findings revealed that hospitalization costs were significantly reduced in the CKD-DM-HF comorbidity subgroup, whereas both hospital stays and costs declined in the HF subgroup following system deployment. These beneficial outcomes may be attributed to timely monitoring and standardized serum potassium management, which mitigates the need for ancillary interventions including urgent dialysis, repeated laboratory testing, and supportive care for complications linked to refractory hyperkalemia. In contrast, although hospital stays and hospitalization costs in the CKD subgroup exhibited a downward trend post-implementation, these changes did not attain statistical significance. This lack of significance may be driven by secular trends, evolving clinical practices, interindividual variations in medication use, adjustments to health insurance policies, and other time-dependent confounding factors. To further elucidate the independent determinants of prolonged hospital stays and elevated hospitalization costs, binary logistic regression analyses were performed. Notably, our regression models identified prolonged hospital stays as the strongest independent predictor of elevated hospitalization costs, aligning with established clinical evidence that longer hospital stays are directly correlated with higher medical expenditures [ 35 ]. In this study, the implementation of the Serum Potassium Management Center System was independently associated with a 27.3% lower risk of prolonged hospital stay; moreover, each 1-unit elevation in serum potassium was independently linked to a 29.4% reduced risk of prolonged hospital stay, revealing a significant inverse association between serum potassium levels and hospital stays. The inverse association between serum potassium and hospital stays may be explained by several mechanisms. First, patients with higher serum potassium levels in this cohort likely received more timely and aggressive interventions for hyperkalemia, including targeted potassium-lowering therapies and intensive laboratory monitoring. Such prompt interventions rapidly corrected electrolyte disturbances, prevented clinical deterioration, and consequently shortened the duration of hospitalization. Second, this observed association may reflect differences in baseline disease severity and overall clinical stability rather than a direct beneficial effect of hyperkalemia itself, as patient-level clinical heterogeneity could modulate both serum potassium dynamics and hospital length of stay. Of note, the system itself did not exert a direct independent effect on hospitalization costs in the multivariate model. Collectively, these findings support a mediated causal pathway rather than a direct effect: the integrated serum potassium management system may indirectly reduce hospitalization costs primarily by shortening hospital stays, which subsequently curtails related expenses such as ward fees, routine monitoring, laboratory assays, and supportive care. In essence, the cost-saving benefit of the system is likely mediated through its impact on reducing prolonged hospitalization, rather than via a direct independent effect on medical costs. This paradigm also clarifies why the system did not emerge as a significant direct predictor in the cost model: its economic advantage is channeled through the intermediate outcome of shorter hospital stays, which constitutes the dominant driver of medical costs in this cohort. Therefore, even though the system did not independently predict lower hospitalization costs after adjusting for length of stay, it still effectively alleviates the economic burden by preventing prolonged hospital stays – the core contributor to elevated costs among this high-risk patient population. 5. Limitations This study has several limitations. First, this was a single‑center, retrospective study with a historical‑control design. Such a design is inherently susceptible to secular trends and unmeasured confounding factors, including temporal changes in clinical practice, staffing, hospital policies, laboratory workflows, discharge criteria, and medical financing systems, all of which may have influenced hospitalization costs and hospital stays. Therefore, definitive causal relationships between the intervention and the observed outcomes cannot be established. Second, important baseline variables that may affect comparability were not fully available for adjustment, including admission service, principal diagnosis, acute versus chronic hyperkalemia, ICU admission, severity of CKD/HF/DM, acid‑base status, prior dialysis, medication burden beyond RAASi/MRA, and whether hyperkalemia was present on admission or hospital‑acquired. These factors may strongly influence management intensity and healthcare resource utilization, further limiting comparability between groups. Third, subgroup analyses for hospital stays and hospitalization costs were exploratory and underpowered, and no adjustment for multiple comparisons was performed. Some statistically significant findings may therefore have occurred by chance alone, and these results should be interpreted as preliminary and exploratory rather than conclusive evidence of intervention effectiveness. Fourth, the definition of potassium‑lowering treatment was broad and heterogeneous, including temporizing measures, diuretics, and dialysis, without distinguishing indication, timing, or clinical appropriateness. Some interventions may have addressed underlying volume status or renal failure rather than hyperkalemia specifically. In addition, reductions in post‑treatment potassium levels should be interpreted cautiously, as they may reflect regression to the mean, spontaneous resolution, or variations in testing intervals rather than specific therapeutic effects. Fifth, the observed improvements in diagnosis, treatment, and follow‑up rates may partially reflect enhanced documentation and modified workflow behavior driven by system prompts, rather than genuine improvements in clinical recognition or patient‑centered outcomes. Hard clinical endpoints such as mortality, arrhythmia, or emergency dialysis were not evaluated. Sixth, the treatment rate of hyperkalemia might have been underestimated, as the study did not capture medication adjustments or low‑potassium diet counseling following diagnosis, both of which are key components of routine clinical management. 6. Conclusions Hyperkalemia continues to represent a key clinical priority, with ongoing advances in its diagnosis, treatment, and assessment of its clinical and economic burden. Standardized management strategies are crucial for optimal care of affected patients. The implementation of the ‘Serum Potassium Management Center System’ represents an innovative approach to hyperkalemia management, offering a standardized model that was associated with improved diagnostic identification, treatment implementation, and follow-up testing rates in this study. The system was also associated with favorable trends in hospital length of stay and hospitalization costs in several high‑risk subgroups. Nonetheless, challenges remain, including inconsistent clinician awareness and non‑standardized approaches to hyperkalemia management. The standardized pathway enabled by this system may help overcome these gaps and streamline core components of hyperkalemia care. Notably, this study was limited to process indicators and disease burden-related measures, and hard clinical outcomes such as mortality, arrhythmia, ICU transfer, or emergency dialysis were not evaluated. Therefore, the findings should be interpreted as observational associations rather than definitive improvements in hard clinical outcomes. Further prospective and comparative studies are warranted to validate whether this system can translate process improvements into better clinical outcomes, reduce the disease burden, and improve patients’ quality of life and survival. Acknowledgements The authors would like to thank the Kidney Disease Data Center for their cooperation and all of the staff members who assisted. Guangna Lyu, Rongshan Li, and Xiaoshuang Zhou contributed to the study design and definition of intellectual content. Guangna Lyu, Fang Zhang, and Xiuzhao Fan were involved in data acquisition, data analysis, statistical analysis, and interpretation. The manuscript was written by Guangna Lyu and reviewed by Rongshan Li and Xiaoshuang Zhou. All authors contributed to the article, reviewed the manuscript, and approved the submitted version. 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