Prediction of Paroxysmal Atrial Fibrillation With Incorporating Genomic Information Into AI-Based ECG Analysis.

BACKGROUND

It remains unclear whether incorporating genetic information and blood biomarkers can improve artificial intelligence (AI) models predicting paroxysmal atrial fibrillation (PAF) from sinus rhythm electrocardiograms (ECGs).

OBJECTIVES

This study aimed to explore the potential value of polygenic risk scores (PRS) and blood biomarkers in AI-based ECG (AI-ECG) analysis for predicting PAF.

METHODS

Patients from 7 institutions in Japan were recruited between March 1, 2020 and December 31, 2021. AI-ECG scores and PRS were calculated using previously reported AI model and PRS algorithm. High-sensitivity C-reactive protein, N-terminal pro-B-type natriuretic peptide, and cell-free DNA (cfDNA) levels were also evaluated.

RESULTS

A total of 2,128 cases were included (PAF: 1,055 of 2,128 [49.6%]). AI-ECG scores were significantly higher in PAF group than in non-AF group, with an area under the curve (AUC) of 0.882 (95% CI: 0.866-0.897) for the receiver-operating characteristic (ROC) curve in predicting PAF. PRS was also significantly higher in PAF group, with AUC-ROC of 0.655 (95% CI: 0.630-0.680). Although combining AI-ECG scores and PRS did not improve AUC-ROC, a significant improvement was observed in the net reclassification improvement of 0.467 (95% CI: 0.374-0.558) and integrated discrimination improvement of 0.028 (95% CI: 0.020-0.035). Among blood biomarkers, only cell-free DNA, along with PRS and AI-ECG, was associated with PAF in multivariable analysis; however, its contribution to PAF prediction was limited.

CONCLUSIONS

Genetic information may provide complementary insights and improve risk stratification for the prediction of PAF using AI-ECG.