Interpretable machine learning models for predicting perioperative myocardial injury in non-cardiac surgery.

Abstract

Aims: Perioperative myocardial injury (PMI) is a frequent and often asymptomatic complication after non-cardiac surgery and is associated with increased short- and long-term mortality. Conventional risk scores, such as the Revised Cardiac Risk Index (RCRI), have limited predictive accuracy and are infrequently used in clinical practice. We aimed to develop and temporally validate an interpretable machine learning model using Explainable Boosting Machines (EBMs) to predict PMI from routine pre-operative data.

Methods and results: In this retrospective cohort study at a tertiary care centre in Germany, we included 9323 adult patients undergoing 9824 non-cardiac surgical procedures between 2014 and 2023 who received post-operative high-sensitivity cardiac troponin testing as part of routine care. PMI was defined as a post-operative elevation of high-sensitivity cardiac troponin above the upper reference limit. An EBM was trained on structured pre-operative data from 2014 to 2021 and evaluated in a temporally independent test cohort from 2022 to 2023, with performance compared with logistic regression, random forest, XGBoost, and a modified RCRI. Model discrimination, calibration, and Brier scores were assessed. Feature contributions were examined using internal shape functions and SHAP values. PMI occurred in 2804 procedures (28.5%). The EBM achieved the highest predictive performance (AUROC 0.730, 95% CI 0.720-0.740), outperforming all comparators. Calibration was robust across clinically relevant risk ranges. Key predictors included age, leukocyte count, renal function, potassium, and platelet count. The EBM identified high-risk patients more efficiently than the modified RCRI and ESC guideline-based strategies (Number Needed to Evaluate 3.0 vs. 3.5) and reduced troponin assays by 18.2% in the temporally independent cohort.

Conclusion: An interpretable machine learning model trained on routine clinical data can accurately predict PMI and outperform existing risk scores. The EBM supports individualized risk stratification and may enhance perioperative decision-making and resource allocation within a guideline-directed testing population. Prospective and external validation is required before clinical implementation.