Machine learning and conventional Cox regression to predict target-lesion revascularization after percutaneous coronary intervention.

Introduction: Despite advances in procedural and medical treatment, the risk of target-lesion revascularization (TLR) after percutaneous coronary intervention (PCI) persists. This study investigated the use of machine learning (ML)-based Least Absolute Shrinkage and Selection Operator (LASSO) to predict the risk of short- and long-term clinically driven TLR compared with conventional Cox regression with stepwise variable selection. Methods: The dataset consisted of 24,360 patients with 34,149 Results: Full Cox and stepwise Cox performed equally at 0-1 years (Harrell's C 0.6743). Cox-LASSO provided a minor improvement in predictive performance on the short-term risk of TLR (0.6774). At 1-5 years, stepwise Cox had the best predictive performance (0.6831) and was not outperformed by Cox-LASSO (0.6818). Most identified risk factors for TLR were consistent across conventional Cox models and Cox-LASSO. Survival curves showed separation between high- and low-risk index lesions in all models, as evaluated by the log-rank test. Conclusion: The ML-based Cox-LASSO model did not improve predictive performance over well-specified conventional Cox regression models for short- and long-term TLR. The models demonstrated intermediate predictive performance and suggest that they can support risk stratification after further validation, but they may not yet be precise enough for definitive bedside decision-making for individual patients.