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.
View Article and Find Full Text PDFIntroduction: For acute coronary syndrome (ACS) patients treated with percutaneous coronary intervention (PCI), the choice of the duration and kind of dual antiplatelet therapy (DAPT) offering the most accurate balance between ischemic and bleeding risk remains unknown.
Evidence Acquisition: A network meta-analysis was performed including all Randomized Controlled Trials (RCTs) comparing different DAPT regimens and duration in ACS patients undergoing PCI. Trial-defined MACE and major bleedings were the primary endpoints.