Online Clinical Calculator for Predicting 28-Day Mortality in Older Adult Patients With Sepsis-Associated Encephalopathy: Retrospective Study Using MIMIC-IV
Bibliographic record
Abstract
BACKGROUND: Sepsis-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. OBJECTIVE: We 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. METHODS: Using 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. RESULTS: A 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. CONCLUSIONS: This 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".