Development and validation of a machine learning model for post-PCI exercise intolerance in patients with coronary...

Exercise intolerance after percutaneous coronary intervention (PCI) is a common yet often overlooked condition in patients with coronary artery disease (CAD), associated with impaired cardiopulmonary recovery and poor prognosis. However, an accurate and easily applicable non-exercise-based model for predicting post-PCI exercise intolerance remains lacking. This study aimed to develop and validate such a model using electronic medical record (EMR) data.

Between June 2020 and June 2024, clinical data were retrospectively collected from Quanzhou First Hospital. Forty-five variables were considered as candidate predictors, and seven machine learning algorithms were developed to estimate the risk of post-PCI exercise intolerance. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PRC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Calibration and clinical utility were assessed via calibration plots, Brier score, Hosmer-Lemeshow (H-L) goodness-of-fit test, and decision curve analysis. Model interpretability was examined using Shapley additive explanations, and an interactive web-based calculator was deployed for clinical use.

A total of 575 patients were included, with an incidence of exercise intolerance of 22.0%. Eight key variables were selected: age, sex, BMI, smoking status, diabetes status, hemoglobin level, red blood cell count, and resting heart rate. The multilayer perceptron (MLP) model achieved the best performance (threshold = 0.30): an AUC-ROC of 0.911 (0.854-0.956), an AUC-PRC of 0.706 (0.548-0.846), an accuracy of 0.87, a sensitivity of 0.82, a specificity of 0.88, a PPV of 0.67, and an NPV of 0.94 (Brier = 0.108; H-L test p = 0.493).

The proposed EMR-based model effectively identifies patients at high risk of post-PCI exercise intolerance, supporting early screening and targeted clinical interventions.