An extreme gradient boosting machine learning model significantly outperformed the Revised Cardiac Risk Index in predicting myocardial injury after lung cancer surgery (AUC 0.795 vs 0.595).
Cohort (n=1,644)
Yes
Does a preoperative machine learning model improve the prediction of myocardial injury after non-cardiac surgery compared to the Revised Cardiac Risk Index in patients undergoing lung cancer surgery?
A preoperative machine learning model using five routine clinical features significantly outperformed the Revised Cardiac Risk Index in predicting myocardial injury after lung cancer surgery.
Absolute Event Rate: 0.795% vs 0.595%
Myocardial injury after non-cardiac surgery (MINS) is a common complication following lung cancer surgery and is associated with adverse prognostic outcomes. Preoperative cardiac risk assessment is essential for optimizing perioperative management and improving patient outcomes. This study aimed to develop and validate an interpretable machine learning (ML) model using preoperative clinical features to predict MINS following lung cancer surgery. Patients undergoing elective lung cancer surgery from two medical centers were included. Preoperative clinical data were retrospectively collected. Multiple feature selection methods were applied to identify the final predictors. Prediction models were constructed using eight ML algorithms and compared with the Revised Cardiac Risk Index (RCRI). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. The SHapley Additive exPlanations (SHAP) framework was used to enhance model interpretability. A total of 1644 patients were included. Five preoperative predictors were identified: male sex, age, neoadjuvant therapy, leukocyte count, and high-sensitivity cardiac troponin T (hs-cTnT). In the external validation cohort, the extreme gradient boosting (XGB) model achieved the best generalization performance (AUC = 0.795), significantly outperforming the RCRI (AUC = 0.595). In the high-risk subgroup (RCRI score ≥ 2), the random forest model showed further improved discriminative ability (AUC = 0.837). SHAP analysis identified hs-cTnT and age as the most important predictors. The model was deployed as a user-friendly web-based calculator for clinical application. This study successfully developed and validated an interpretable ML model using routinely available preoperative indicators, which significantly outperformed the conventional RCRI in predicting MINS after lung cancer surgery. This tool provides effective decision support by improving preoperative cardiac risk stratification.
Yuan et al. (Tue,) conducted a cohort in Myocardial injury after non-cardiac surgery (MINS) following lung cancer surgery (n=1,644). Machine learning model (XGB) vs. Revised Cardiac Risk Index (RCRI) was evaluated on Prediction of myocardial injury after non-cardiac surgery (AUC). An extreme gradient boosting machine learning model significantly outperformed the Revised Cardiac Risk Index in predicting myocardial injury after lung cancer surgery (AUC 0.795 vs 0.595).
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