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 were calculated and plotted to interpret the contributions of individual characteristics to the model's predictions.

Findings: Seventy-seven predictive characteristics were analyzed. The stacking model achieved superior performance with AUCs 0·931 and F1 scores 0·797 in the independent external validation, outperforming CHA2DS2-VASc, HATCH, and POAF scores (AUC 0·931 vs. 0·713, 0·708, and 0·667; p < 0·05). SHAP value indicate that the importance of predictive features for NOAF, in descending order, include: Brain natriuretic peptide, Left ventricular end-diastolic diameter, Ejection fraction, BMI, β-receptor blockers, Duration of surgery, Age, Neutrophil percentage-to-albumin ratio, Myocardial infarction, Left atrial diameter, Hypertension, and smoking status. Subsequently, we constructed an easy-to-use bedside clinical tool for NOAF risk assessment leveraging these characteristics.

Interpretation: The AI-based tool offers superior prediction of NOAF, outperforming three existing predictive tools. Future studies should further explore how various patient characteristics influence the timing of NOAF onset, whether early or late.

Funding: This work was funded by Lingnan Nightingale Nursing Research Institute of Guangdong Province, and Guangdong Nursing Society (GDHLYJYZ202401).