ArticleJMIR medical informatics2025
Online Clinical Calculator for Predicting 28-Day Mortality in Older Adult Patients With Sepsis-Associated Encephalopathy: Retrospective Study Using MIMIC-IV.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development and external validation of a prediction model for major adverse kidney events within 30 days in sepsis associated acute kidney injury: a multi-center retrospective clinical study.BMC medical informatics and decision making · 2026Article
- Machine learning prediction of in-hospital mortality risk among hospitalized patients with secondary bloodstream infection: a retrospective cohort study.BMC infectious diseases · 2026Article
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5 authors.
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Abstract
backgroundSepsis-associated encephalopathy (SAE) represents a critical complication of sepsis, especially among older adults. Despite its clinical relevance, there remains a lack of accessible and practical tools specifically designed to predict 28-day mortality in this vulnerable population.
objectiveWe aimed to enhance the practical applicability of the model by creating a web-based tool that allows real-time, individualized mortality risk prediction, facilitating early intervention and informed decision-making in clinical practice.
methodsUsing data extracted from the MIMIC-IV (Medical Information Mart for Intensive Care IV) database, we identified older patients (≥65 years) with SAE (n=2165) and divided them into a development cohort (n=1531) and a validation cohort (n=634). Key risk factors associated with 28-day mortality were identified, and a predictive nomogram was constructed. Model performance was evaluated using the concordance index, integrated discrimination improvement, net reclassification index, and calibration curve analysis. Clinical applicability was assessed through decision curve analysis and benchmarked against traditional intensive care unit (ICU) scoring systems. Furthermore, the nomogram was deployed as a web-based application, enabling clinicians to input data and generate individualized mortality predictions.
resultsA total of 2165 older patients with SAE were included, among whom 290 (13.4%) died within 28 days of ICU admission. Multivariable logistic regression identified lower body weight (odds ratio [OR] 0.985, 95% CI 0.975-0.994; P=.001), lower systolic blood pressure (OR 0.972, 95% CI 0.957-0.986; P<.001), lower hemoglobin (OR 0.984, 95% CI 0.974-0.995; P=.005), lower PaO2 (OR 0.996, 95% CI 0.994-0.997; P<.001), and lower Glasgow Coma Scale score (OR 0.825, 95% CI 0.786-0.864; P<.001) as mortality risk factors. Higher respiratory rate (OR 1.083, 95% CI 1.029-1.141; P=.002), increased anion gap (OR 1.081, 95% CI 1.031-1.135; P=.001), elevated blood urea nitrogen (OR 1.045, 95% CI 1.016-1.076; P=.002), prolonged partial thromboplastin time (OR 1.033, 95% CI 1.016-1.050; P<.001), and reduced urine output (OR>0.99, 95% CI 0.999-1.000; P=.002) were also predictive. Patients admitted to "other" ICU types had lower mortality compared with the medical ICU reference group (OR 0.327, 95% CI 0.176-0.609; P<.001). The nomogram achieved concordance index values of 0.899 (development) and 0.897 (validation), outperforming sequential organ failure assessment (0.692), Acute Physiology Score III (0.804), Logistic Organ Dysfunction System (0.771), Simplified Acute Physiology Score II (0.704), and Oxford Acute Severity of Illness Score (0.753), with significant integrated discrimination improvement and net reclassification index improvements (all P<.001). Calibration curves confirmed good agreement between predicted and observed outcomes, while decision curve analysis supported the model's superior clinical utility.
conclusionsThis study presents a novel, validated nomogram for predicting 28-day mortality in older patients with SAE, integrating routinely available clinical data. The deployment of the model as a digital tool enhances its accessibility and usability, providing clinicians with a practical resource for risk stratification and individualized patient management.
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