Abstract Details

Title Machine Learning Models for Mortality and Readmission in Encephalopathy: Performance and Pitfalls From a Large ICU Cohort

Topic Neuro Trauma and Critical Care

Presentation(s) P6 - Poster Session 6 (5:00 PM-6:00 PM)

Poster/Presentation
Number 19-009

Objective To develop and evaluate electronic health record (EHR)–based machine learning models for predicting key adverse outcomes—including in-hospital mortality, 30-day mortality, ICU readmission, composite adverse outcomes, and poor functional recovery—among critically ill adults with encephalopathy. The study aimed to identify the best-performing model architecture, determine the most predictive clinical features, and highlight methodological pitfalls such as data leakage and spectrum bias that may influence model performance and generalizability.

Background

Encephalopathy among critically ill adults is common and portends worse outcomes, yet bedside prognostic tools are limited. We evaluated electronic health record (EHR)–derived machine-learning models to estimate risk of mortality and ICU readmission in this population.

Design/Methods In a retrospective cohort from MIMIC-IV, we identified 11,468 patients with encephalopathy across 15,630 ICU stays. From demographics, vital signs, laboratory values, medication administrations, and mental-status assessments (217 features), we trained five machine learning model families (random forest, gradient boosting, logistic regression, support-vector machine, neural network) to predict hospital mortality, 30-day mortality, ICU readmission, a composite adverse outcome, and poor functional outcome. Models were developed with non-overlapping train–test splits designed to mitigate leakage. Discrimination was summarized as AUROC with 95% CIs.

Results

Random forests yielded the highest internal discrimination. AUROC (95% CI) was 0.984 (0.979–0.989) for hospital mortality, 0.985 (0.980–0.990) for ICU readmission, 0.971 (0.965–0.977) for the composite outcome, 0.905 (0.895–0.915) for 30-day mortality, and 0.874 (0.862–0.886) for poor functional outcome. Highly predictive features included age, illness-severity indicators, ICU type, and early instability in vital signs.

Conclusions

Machine learning models achieved high discrimination for mortality and readmission among ICU patients with encephalopathy in internal testing. Given the risk of hidden leakage and spectrum effects in single-center retrospective data, external and prospective validation with calibration, decision-curve analysis, subgroup/fairness evaluation, and comparison to established scores is required before any clinical use.

KEYWORDS: artificial intelligence: machine learning; prognostic modeling; encephalopathy; critical care.

Authors/Disclosures
Shankar Biswas, MD PRESENTER	Dr. Biswas has nothing to disclose.
Sindhu Vasireddy, MD	Dr. Vasireddy has nothing to disclose.
Elangovan Krishnan, MBBS, PhD, PGDHM, M Tech	Dr. Krishnan has nothing to disclose.
Riya Srivastava	Ms. Srivastava has nothing to disclose.
Ayman Hamadttu, MBBS	Mr. Hamadttu has nothing to disclose.
Jeimy M. Castellanos, MBBS	Dr. Castellanos has nothing to disclose.

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