Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Abstract

Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on clinical, anatomical, and procedural features to predict all-cause mortality following contemporary bifurcation PCI. Multiple ML models to predict all-cause mortality were tested on a cohort of 2393 patients (training, n = 1795; internal validation, n = 598) undergoing bifurcation PCI with contemporary stents from the real-world RAIN registry. Twenty-five commonly available patient-/lesion-related features were selected to train ML models. The best model was validated in an external cohort of 1701 patients undergoing bifurcation PCI from the DUTCH PEERS and BIO-RESORT trial cohorts. At ROC curves, the AUC for the prediction of 2-year mortality was 0.79 (0.74–0.83) in the overall population, 0.74 (0.62–0.85) at internal validation and 0.71 (0.62–0.79) at external validation. Performance at risk ranking analysis, k-center cross-validation, and continual learning confirmed the generalizability of the models, also available as an online interface. The RAIN-ML prediction model represents the first tool combining clinical, anatomical, and procedural features to predict all-cause mortality among patients undergoing contemporary bifurcation PCI with reliable performance.

Long‐term clinical outcomes following successful percutaneous coronary intervention in patients with extremely long coronary chronic total occlusion lesions

“…There have been some mid‐ to long‐term studies on the prognosis according to lesion length in patients with CTO lesion, and it has been reported that lesion length is associated with adverse clinical outcomes such as revascularization and MACE 19,20 . However, including our previous study, none of the studies on lesion length and long‐term prognosis of CTO can demonstrate a correlation between hard endpoints such as cardiac mortality and lesion length 20–22 . In addition, in the most recently reported multicenter study on the relationship between lesion length and clinical outcomes, the cut‐off for the length of the lesion was set at 20 mm, and the study only reported the association between lesion length and in‐hospital or periprocedural outcomes 23 .…”

“…19,20 However, including our previous study, none of the studies on lesion length and long-term prognosis of CTO can demonstrate a correlation between hard endpoints such as cardiac mortality and lesion length. [20][21][22] In addition, in the most recently reported multicenter study on the relationship between lesion length and clinical outcomes, the cut-off for the length of the lesion was set at 20 mm, and the study only reported the association between lesion length and in-hospital or periprocedural outcomes. 23 However, in this study, unlike the results of recent studies that evaluated the role of lesion length on clinical outcomes, the incidence of hard endpoints such as cardiac mortality and mortality were also found to be frequent in long lesions.…”