Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy.

ABSTRACT: Introduction: The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance D_PS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized m

SUBMITTER: Dokuchaev A 

PROVIDER: S-EPMC10367108 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature