Development and Validation of an Interpretable Machine Learning Model to Predict Mortality in Patients With Sepsis-Induced Coagulopathy: Multicenter Cohort Study.

Published in JMIR medical informatics. Abstract: Sepsis-induced coagulopathy (SIC) is a common and severe complication in patients with sepsis, characterized by microvascular thrombosis, systemic endothelial damage, and markedly increased short-term mortality. Existing traditional clinical risk scoring systems demonstrate limited accuracy and fail to capture complex, nonlinear physiological interactions, underscoring the urgent need for advanced prognostic tools. The objective of this study was to develop and validate an interpretable machine learning (ML) model using large-scale, multicenter databases to predict early mortality in intensive care unit (ICU) patients with SIC and to evaluate its predictive performance and clinical utility compared with traditional clinical risk scores. The study retrospectively analyzed clinical data of patients with SIC from the Medical Information Mart for Intensive Care IV (MIMIC-IV), the eICU Collaborative Research Database (eICU-CRD), and Tianjin Medical University General Hospital. Feature selection was performed using LASSO (least absolute shrinkage and selection operator) regression, the Boruta algorithm, and recursive feature elimination with cross-validation, combined with multivariable logistic regression. Twelve ML algorithms were trained and compared with 4 traditional clinical scoring systems (Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II, and Oxford Acute Severity of Illness Score) to predict 28-day...