Development and validation of an interpretable machine learning model with multicenter routine tests to predict major adverse cardiac and cerebrovascular events after cardiac surgery

Systemic inflammatory response and coagulation disorder triggered by cardiac surgery under cardiopulmonary bypass (CPB) may increase the risk of major adverse cardiovascular and cerebrovascular events (MACCE). This study aims to develop and validate an interpretable machine learning model based on the inflammation–coagulation axis using routine laboratory data to predict postoperative MACCE. This multicenter retrospective cohort study included patients undergoing CPB cardiac surgery at four tertiary cardiac centers in China between 2019 and 2023. Predictors included patients’ baseline characteristics, perioperative blood counts and routine coagulation parameters. Multiple machine learning models were developed and evaluated, with the Explainable Boosting Machine (EBM) selected for global and local interpretability analyses and sensitivity analyses. A total of 27,847 patients were included, with 26,687 assigned to the training set and 1,160 to the validation set. The EBM model achieved the best performance among all models (AUC 0.789; 95% CI, 0.724–0.847). Top important predictors included postoperative lymphocyte percentage, preoperative thrombin time, postoperative international normalized ratio, lymphocyte count, red blood cell distribution width–standard deviation, fibrinogen, prothrombin time, and plateletcrit. EBM effectively predicts MACCE after cardiac surgery and identifies key biomarkers related to the inflammation–coagulation axis as an early postoperative risk-stratification tool. Further validation in larger multicenter prospective studies is warranted.