Performance of an AI prediction tool for new-onset atrial fibrillation after coronary artery bypass grafting.

ABSTRACT:

Background

There is lack of tools to predict new-onset postoperative atrial fibrillation (NOAF) after coronary artery bypass grafting (CABG). We aimed to develop and validate a novel AI-based bedside tool that accurately predicts predict NOAF after CABG.

Methods

Data from 2994 patients who underwent CABG between March 2015 and July 2024 at two tertiary hospitals in China were retrospectively analyzed. 2486 patients from one hospital formed the derivation cohort, split 7:3 into training and test sets, while the 508 patients from a separate hospital formed the external validation cohort. A stacking model integrating 11 base learners was developed and evaluated using Accuracy, Precision, Recall, F1 score, and Area Under Curve (AUC). SHapley Additive exPlanations (SHAP) values w

SUBMITTER: Ma H 

PROVIDER: S-EPMC11908608 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature