Predicting the rapid progression of coronary artery lesions in patients with acute coronary syndrome based on machine learning - Gui, Long; Hu, Yuekang; Ouyang, Hua; Zhuang, Huanwei; Peng, Yangfei; Yang, Jun; Cao, Heshan; Yang, Songran; Hua, Ping (Frontiers in Cardiovascular Medicine, 2025)
PurposesRapid coronary artery lesions (RCAL) are strongly linked to major adverse cardiovascular events in patients with acute coronary syndrome (ACS). This work developed a public online prediction platform for RCAL (OpRCAL) by comparing the performance of nine machine learning models.MethodsWe retrospectively examined the clinical data of 324 patients with ACS who received percutaneous coronary intervention (PCI). Using both univariate and multivariate analyses, the potential independent risk factors for RCAL were studied. Following the screening of all variables using Lasso regression, multiple machine learning models were constructed. The optimal model was then chosen and validated using an external cohort. Furthermore, to elucidate the contribution of each feature to the model, the shapley additive explanation (SHAP) values of the variables were calculated. Finally, a prediction platform for RCAL in patients with ACS following PCI was established.ResultsThe number of coronary lesions, systolic blood pressure (SBP), N-terminal pro-brain natriuretic peptide (NT-proBNP), QRS interval, and platelet count were found as independent risk factors for RCAL. Among the nine machine learning models constructed after identifying twelve different variables using Lasso regression, the random forest (RF) model performed best in the training cohort and showed good generalization in the external test cohort, with area under curve of 0.774 (95%CI: 0.640–0.909). Finally, we constructed an online platform named OpRCAL for clinicians to predict RCAL in patients with ACS following PCI based on the RF model.ConclusionsThe RF model exhibits high accuracy and generalizability in predicting RCAL, thereby providing a valuable instrument to assist clinical decision-making.