Data Sheet 1_Establishment and validation of an interpretable machine learning-based predictive model for risk of post-PCI in-hospital heart failure in AIHD patients.doc

BackgroundThis study intends to establish and validate an interpretable machine learning (ML) model based on clinical features for early prediction of the risk of post-percutaneous coronary intervention (PCI) in-hospital heart failure (HF) in patients with acute ischemic heart disease (AIHD).
MethodsThis study retrospectively included AIHD patients who underwent PCI at the Affiliated Guangzhou Hospital of TCM of Guangzhou University of Chinese Medicine from January 2023 to May 2025. LASSO regression was utilized for feature screening first, and then seven predictive models for HF risk in AIHD patients were established using ML algorithms. The model performance was fully assessed on the validation set through the area under the curve (AUC) with 95% CI, calibration curve and expected calibration error, recall, F1-score, positive predictive value, negative predictive value, and accuracy, and internal validation was conducted using the Bootstrap method. In addition, feature importance was evaluated by SHapley Additive exPlanations (SHAP) values, and individualized predictions were explained by Local Interpretable Model-Agnostic Explanations (LIME).
ResultsTwo hundred and three patients with AIHD were ultimately included, of whom 55 (27.1%) experienced in-hospital HF. Of the seven ML models, the random forest (RF) model demonstrated optimal performance on the validation set, with an AUC of 0.70 (95% CI 0.53–0.84) and an accuracy of 0.77; the calibration curve revealed high agreement between predicted and actual risks. Twelve predictive features associated with endpoint events were identified by LASSO regression, and the top five features contributing to the predictive efficacy of the RF model were age, monocyte count, heart rate, platelet count, and mean platelet volume according to the ranking of feature importance. In addition, the contribution of features to the prediction of HF risk was visualized by SHAP summary plots and LIME.Finally, an open Web-based prediction tool was deployed.
ConclusionThis exploratory study developed a random forest (RF) model to predict the risk of post-PCI in-hospital HF in patients with AIHD. Based on the SHAP and LIME methods, the clinical interpretability of the model was significantly enhanced. Future research with larger sample sizes is warranted to optimize the training set and validate the generalizability of the model.