Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model.

Angio-based microvascular resistance (AMR) as a potential alternative to the index of microcirculatory resistance (IMR) and its relationship with microvascular obstruction (MVO) and other cardiac magnetic resonance (CMR) parameters still lacks comprehensive validation. This study aimed to validate the correlation between AMR and CMR-derived parameters and to construct an interpretable machine learning (ML) model, incorporating AMR and clinical data, to forecast MVO in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PPCI). We enrolled 452 STEMI patients from Nanjing Drum Tower Hospital between 2018 and 2022, who received both PPCI and CMR. After PPCI, AMR measurements and CMR-derived parameters were recorded, and clinical data were gathered. The ML workflow comprised feature selection using the Boruta algorithm, model construction with seven classifiers, hyperparameter optimization via ten-fold cross-validation, model comparison based on the area under the curve (AUC), and a Shapley additive explanations (SHAP) analysis to analyze the significance of different features. 32.29% of patients showed inconsistency between AMR and MVO, but we successfully constructed a predictive model for MVO. Among the classifiers, Extreme gradient boosting (XGBoost) post hyperparameter optimization displayed superior performance, achieving an AUC of 0.911 and 0.846 in the training and validation sets, respectively. SHAP analysis identified AMR as a pivotal predictor of MVO. Although we observed the inconsistency between AMR and MVO but the ML-based construction of MVO prediction model is feasible, which brings the possibility of timely prediction of patients with MVO and timely imposition of interventions during PPCI.