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

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.