Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers.

ABSTRACT:

Background

Postoperative atrial fibrillation (POAF) is a frequent complication following coronary artery bypass grafting (CABG), significantly impacting patient prognosis and healthcare costs. This study aimed to develop an integrated predictive model for POAF risk stratification to optimize clinical management.

Methods

We retrospectively analyzed 2,528 patients undergoing 21-gene pharmacogenetic testing for cardiovascular therapy. After stringent data curation, 576 CABG patients were enrolled and randomly allocated into training and test sets. Eight machine learning algorithms were trained using clinical variables and genetic variants. An independent validation set was performed on 61 patients from a subsequent 1,075-patient cohort of 21-gene pharmacogenetic testing.

Res

SUBMITTER: Hua L 

PROVIDER: S-EPMC12460236 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature