Prognostic prediction of cardiovascular adverse events in patients after percutaneous coronary intervention using machine learning

Abstract Introduction While prevention of major cardiovascular adverse events (MACE) is important in the management of patients after percutaneous coronary intervention (PCI), risk stratification of MACE remains a significant challenge with increasingly diverse and complex patient backgrounds. In response to that, this retrospective observational study aimed to explore whether machine learning (ML) methods can predict future MACE after PCI using a variety of features extracted from electronic medical records (EMRs). Methods In 3,016 patients with coronary artery disease who performed PCI, 3 machine learning (ML) algorithms (gentle boost, random forest, and neural network) and statistical model were retrospectively applied to predict the occurrence of MACE within two years after PCI (564 patients, 18.7%). MACE was defined as composite endpoint of cardiac death, myocardial infarction (MI), any revascularization, and congestive heart failure (CHF). Basic patient information (e.g., age, sex, coronary risk factors) and laboratory test results were used for the analysis. Feature importance was calculated to identify key factors for MACE. Hold-out method with an 8:2 split between training and test was used to calculate sensitivity, specificity, positive predictive value, negative predictive value, and F-value for each model. Results As shown in Figure 1, ML models showed excellent diagnostic accuracies in the prediction of MACE after PCI, demonstrating higher F-values compared with statistical model (logistic regression analysis). Similar results were seen in the subgroup analyses for estimating cardiac death, MI, revascularization, or CHF. Although the weighting of feature importance for MACE recurrence varied among ML models, infarct size (peak CK levels), increased inflammatory status, and traditional coronary risk factors were identified as important features for predicting MACE within two years after PCI (Figure 2). Conclusions It is suggested that ML using EMRs is promising for inferring future MACE within two years after PCI. Further studies are warranted to address whether ML-based risk stratification could lead to improved clinical decision-making after PCI.