Machine Learning for Prediction of Technical Results of Percutaneous Coronary Intervention for Chronic Total Occlusion

Abstract

(1) Background: The probability of technical success in percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) represents essential information for specifying the priority of PCI for treatment selection in patients with CTO. However, the predictabilities of existing scores based on conventional regression analysis remain modest, leaving room for improvements in model discrimination. Recently, machine learning (ML) techniques have emerged as highly effective methods for prediction and decision-making in various disciplines. We therefore investigated the predictability of ML models for technical results of CTO-PCI and compared their performances to the results from existing scores, including J-CTO, CL, and CASTLE scores. (2) Methods: This analysis used data from the Japanese CTO-PCI expert registry, which enrolled 8760 consecutive patients undergoing CTO-PCI. The performance of prediction models was assessed using the area under the receiver operating curve (ROC-AUC). (3) Results: Technical success was achieved in 7990 procedures, accounting for an overall success rate of 91.2%. The best ML model, extreme gradient boosting (XGBoost), outperformed the conventional prediction scores with ROC-AUC (XGBoost 0.760 [95% confidence interval {CI}: 0.740–0.780] vs. J-CTO 0.697 [95%CI: 0.675–0.719], CL 0.662 [95%CI: 0.639–0.684], CASTLE 0.659 [95%CI: 0.636–0.681]; p < 0.005 for all). The XGBoost model demonstrated acceptable concordance between the observed and predicted probabilities of CTO-PCI failure. Calcification was the leading predictor. (4) Conclusions: ML techniques provide accurate, specific information regarding the likelihood of success in CTO-PCI, which would help select the best treatment for individual patients with CTO.