Prognostic analysis of sepsis-induced myocardial injury patients using propensity score matching and doubly robust analysis with machine learning-based risk prediction model development.

Background: Sepsis-induced myocardial injury (SIMI) is a severe and common complication of sepsis; However, its definition remains unclear. Prognostic analyses may vary depending on the definition applied. Early prediction of SIMI is crucial for timely intervention, ultimately improving patient outcomes. This study aimed to evaluate the prognostic impact of SIMI and develop validated predictive models using advanced machine learning (ML) algorithms for identifying SIMI in critically ill sepsis patients.

Methods: Data were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.0) database. Patients meeting Sepsis-3.0 criteria were included, and SIMI was defined as troponin T (TNT) levels ≥0.1 ng/mL. Prognostic evaluation involved propensity score matching, inverse probability weighting, doubly robust analysis, logistic regression, and Cox regression. Patients were divided into training and testing datasets in a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection to simplify the model. Twelve hyperparameter-tuned ML models were developed and evaluated using visualized heatmaps. The best-performing model was deployed as a web-based application.

Results: Among 2,435 patients analyzed, 571 (23.45%) developed SIMI following intensive care unit (ICU) admission. Boruta and LASSO identified 46 and 10 key variables, respectively, for prognostic and predictive modeling. Doubly robust analysis revealed significantly worse short- and intermediate-term outcomes for SIMI patients, including increased in-ICU mortality [odds ratio (OR) 1.39, 95% confidence interval (CI) 1.02-1.85,  < 0.05], 28-day mortality (OR 1.35, 95% CI 1.02-1.79,  < 0.05), and 180-day mortality [hazard ratio (HR) 1.21, 95% CI 1.01-1.44,  < 0.05]. However, one-year mortality showed no significant difference (HR 1.03, 95% CI 0.99-1.08,  = 0.169). The XGBoost model outperformed others, achieving an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI 0.79-0.87). SHapley Additive exPlanations (SHAP) analysis highlighted the top five predictive features: creatine kinase-myocardial band (CKMB), creatinine, alanine aminotransferase (ALT), lactate, and blood urea nitrogen (BUN). A web-based application was subsequently developed for real-world use.

Conclusion: SIMI significantly worsens patient prognosis, while the XGBoost model demonstrated excellent predictive performance. The development of a web-based application provides clinicians with a practical tool for timely intervention, potentially improving outcomes for septic patients.