Background: Sepsis-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.
Methods: We 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).
Results: Seven 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.
Conclusion: We 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.
Keywords: Feature selection; Logistic regression; MIMIC-IV database; Machine learning; Mortality prediction; Sepsis-induced cardiomyopathy.
© 2025. The Author(s).