Enhancing prognostic accuracy in sepsis-induced cardiomyopathy: a machine learning approach.

backgroundSepsis-induced cardiomyopathy (SIMD) is a severe yet potentially reversible complication of sepsis, characterized by myocardial dysfunction and associated with high short-term mortality. Conventional scoring systems and traditional statistical models inadequately capture the complex pathophysiology of SIMD, highlighting the need for robust prognostic tools.

methodsWe retrospectively analyzed 1068 adult SIMD patients from the MIMIC-IV database, of whom 236 (22.1%) died within 28 days of ICU discharge. Candidate predictors were screened using Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and Recursive Feature Elimination with Cross-Validation (RFECV). Eight machine learning algorithms were developed and compared. Model performance was evaluated with receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, confusion matrices, and Kolmogorov-Smirnov (K-S) statistics. Model interpretability was assessed with SHapley Additive exPlanations (SHAP).

resultsSeven independent predictors were identified: Acute Physiology Score III (APS III), age, Charlson Comorbidity Index (CCI), cerebrovascular disease, alkaline phosphatase (ALP), lactate, and creatine kinase-MB (CK-MB). Logistic regression achieved consistent discrimination, with AUC values of 0.80 (95% CI 0.79-0.82) in the training set, 0.80 (95% CI 0.77-0.84) in the validation set, and 0.88 (95% CI 0.81-0.94) in the test set. Model accuracies were 70.0%, 67.0%, and 79.0%, respectively, with sensitivities ranging from 0.76-0.82 and specificities from 0.65-0.79. Negative predictive values (NPV) remained high (0.91-0.93), while positive predictive values (PPV) were moderate (0.38-0.52). The K-S statistic indicated strong discrimination (0.46, 0.45, and 0.52 across cohorts). SHAP analysis confirmed APS III (≈0.11), age (≈0.05), and ALP (≈0.03) as the most influential predictors, with CCI (≈0.02) and CK-MB (≈0.01) contributing modest but clinically relevant effects.

conclusionWe established and validated a parsimonious logistic regression model with robust discrimination (AUC up to 0.88) and calibration (K-S > 0.45). The model underscores acute illness severity, aging, and hepatic dysfunction as principal determinants of short-term mortality in SIMD, offering valuable support for early risk stratification in critical care.