Preoperative frailty for predicting in-hospital mortality in patients after cardiac surgery: an interpretable Machine learning model based on a retrospective multicenter cohort study.

BACKGROUND AND OBJECTIVE: Accurate prediction of mortality risk after cardiac surgery is crucial for perioperative management. The laboratory-based frailty index (FI-lab) has been associated with adverse outcomes after surgery, but its predictive value for mortality after cardiac surgery remains to be validated. This study developed a novel interpretable machine learning model integrating FI-lab and routine clinical variables, with rigorous external validation using two independent critical care databases for reliable, clinically transparent risk stratification. METHODS: Data were obtained from the MIMIC-IV and eICU databases. FI-lab was used for frailty evaluation. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select predictive variables, and six machine learning models were developed. Synthetic Minority Oversampling Technique (SMOTE) addressed class imbalance. Model performance was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) values were used for model interpretation. RESULTS: The internal cohort included 7,955 patients, and the external validation cohort included 3,021 patients. LASSO regression identified 37 predictor variables. The Gradient Boosting model performed best, achieving an internal test set AUC of 0.900 (95% CI: 0.868-0.933), sensitivity of 0.844, and specificity of 0.800; external validation set AUC of 0.813 (95% CI: 0.768-0.857), sensitivity of 0.750, specificity of 0.760. SHAP analysis showed that chronic kidney disease (CKD), chronic heart failure (CHF), BUN, FI-lab score, and sex were the most important factors. DCA showed good clinical net benefit in internal validation and had potential application within clinically relevant low-threshold ranges in external validation. CONCLUSION: The FI-lab-based Gradient Boosting model allows for highly accurate prediction of in-hospital mortality risk in patients after cardiac surgery. This externally validated model provides robust evidence for individualized perioperative risk assessment.