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

Fig. 1. Flowchart of patient selection from the MIMIC-IV 2.0 database. From 45,706 septic patients in MIMIC-IV 2.0, those with cardiac comorbidities, missing cTnT data, or cTnT < 0.01 ng/mL were excluded, leaving 3202 with SIMD. After excluding short/long ICU stays and repeat admissions, 1068 patients were included, divided into the 28-day survival group (n = 832) and mortality group (n = 236). MIMIC Medical Information Mart for Intensive Care IV, SIMD sepsis-induced cardiomyopathy, ACS acute coronary syndrome, CHF chronic heart failure, severe tachyarrhythmias include supraventricular tachycardia, ventricular tachycardia, and ventricular fibrillation

Fig. 2. Workflow of predicting 28-day mortality in SIMD using the MIMIC-IV database. After exclusions, 1068 patients were divided into survival (n = 832) and mortality (n = 236) groups. Clinical features were screened by Boruta, LASSO, and RFECV, then tested across multiple classifiers, with logistic regression showing the best performance. SHAP analysis was applied for model interpretation. MIMIC-IV Medical Information Mart for Intensive Care IV, ICU intensive care unit, HR heart rate, RR respiratory rate, SBP systolic blood pressure, DBP systolic blood pressure, MBP mean blood pressure, WBC white blood cell, PLT platelet, CK-MB creatine kinase-muscle brain. CBD cerebrovascular disease, CKD chronic kidney disease, LASSO, least absolute shrinkAge and selection operator, XGBoost extreme gradient boosting; logistic, logistic regression, AdaBoost adaptive boosting; GNB, Gaussian naive Bayes, CNB complementive naive Bayes, SVM support vector machine, KNN nearest neighbor. Created at https://BioRender.com

Fig. 3. Feature selection process for predicting 28-day mortality in patients with SIMD. A LASSO coefficient profiles: The trajectories of coefficients shrink toward zero with increasing penalty parameter (λ), indicating variable selection. B LASSO cross-validation curve: Optimal λ value determined via tenfold cross-validation, minimizing binomial deviance. C Boruta feature importance ranking: Box plots showing confirmed, tentative, and rejected variables, highlighting features with significant predictive value. D RFECV performance curve: Recursive feature elimination with cross-validation identified the optimal number of features based on model accuracy. E Integration of feature selection methods: Venn-type network visualization of key predictors identified by Boruta, LASSO, and RFECV, including APSIII, lactate, ALP, age, CKMB, CCI, and CBD. F Correlation heatmap of predictors. HR, heart rate; SBP, systolic blood pressure; DBP systolic blood pressure, MBP mean blood pressure, RR respiratory rate, WBC white blood cell, Hb hemoglobin, PLT platelet; ALT alanine aminotransferase, AST aspartate aminotransferase, ALP alkaline phosphatase, BUN blood urea nitrogen, TBil total bilirubin, CK-MB…

Fig. 4. Model performance evaluation for predicting 28-day mortality in SIMD. A ROC curves (Training set): Discrimination ability of multiple classifiers, including XGBoost, logistic regression, random forest, AdaBoost, GNB, CNB, SVM, and KNN. B ROC curves (Validation set): External performance comparison of classifiers in the validation cohort. C Decision curve analysis (Validation set): Net clinical benefit across a range of threshold probabilities, showing logistic regression with superior overall utility. D Calibration curves (Validation set): Agreement between predicted and observed outcomes, indicating the goodness of fit for each model. E Precision–recall (PR) curves (Training set): Trade-off between precision and recall in the training cohort, highlighting robustness of logistic regression. F PR curves (Validation set): Precision–recall relationship in the validation cohort, further confirming the comparative performance of classifiers

Fig. 5. Comprehensive performance metrics of machine learning models for predicting 28-day mortality in SIMD. A Training set: Radar-like circular plot summarizing key performance metrics (AUC, accuracy, sensitivity, specificity, F1 score, positive predictive value, and negative predictive value) across different classifiers. B Validation set: External comparison of the same performance indicators in the validation cohort, demonstrating logistic regression with balanced and robust predictive ability across multiple dimensions. AUC area under the ROC curve; KNN k-nearest neighbors, SVM support vector machine, CNB complementive naive Bayes, GNB Gaussian naive Bayes, AB adaptive boosting, RF random ferrost, Log logistic regression, XGB extreme gradient boosting

Fig. 6. Performance evaluation of the logistic regression model for predicting 28-day mortality in SIMD. A ROC curve (Training set): Discriminative ability of the logistic regression model in the training cohort. B ROC curve (Validation set): External validation of model discrimination across multiple folds. C ROC curve (Test set): Predictive performance of the model in the independent test cohort. D Calibration curve (Test set): Agreement between predicted and observed probabilities, showing good calibration. E Decision curve analysis (Test set): Clinical utility of the logistic regression model across different threshold probabilities, indicating positive net benefit. F Learning curve: Stability and robustness of model performance with increasing training samples, showing minimal risk of overfitting.

Fig. 7. Classification performance of the logistic regression model for predicting 28-day mortality in SIMD. A Confusion matrix (Training set): Distribution of true positives, true negatives, false positives, and false negatives in the training cohort. B Confusion matrix (Test set): Classification outcomes in the independent test set, reflecting generalization ability. C Kolmogorov–Smirnov (KS) curve (Test set): Separation between cumulative distributions of positive and negative classes, with a KS statistic of 0.65 at threshold 0.20, indicating strong discriminative power

Fig.8. SHAP-based interpretability analysis of the logistic regression model for predicting 28-day mortality in SIMD. A SHAP heatmap: Patient-level heterogeneity of variable contributions, highlighting APSIII, age, and CBD as major drivers of mortality risk. B SHAP beeswarm plot: Distribution of SHAP values for each predictor, showing APSIII, age, and ALP with the strongest positive influence, while CCI and CKMB contributed less. C Mean absolute SHAP values: Ranking of variable importance confirmed APSIII as the most influential predictor, followed by age and CBD. D SHAP force plots (individual patients): (1) Example case where elevated ALP (20.5), advanced age (84 years), and lower APSIII (49.0) shifted baseline mortality risk moderately, partially offset by CCI = 6.0. (2) Example case where high APSIII (103.0) and CBD = 1.0 strongly increased mortality risk despite high CCI (13.0). APSIII Acute Physiology and Chronic Health Evaluation III, ALP Alkaline Phosphatase, CCI Charlson Comorbidity Index, CBD Cerebrovascular disease; CK-MB Creatine Kinase MB