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 Japan

SUBMITTER: Nakachi T 

PROVIDER: S-EPMC10218988 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature