BibTex format
@article{Sau:2025:10.1001/jamacardio.2025.2522,
author = {Sau, A and Zhang, H and Barker, J and Pastika, L and Patlatzoglou, K and Zeidaabadi, B and El-Medany, A and Khattak, GR and McGurk, KA and Sieliwonczyk, E and Ware, JS and Peters, NS and Kramer, DB and Waks, JW and Ng, FS},
doi = {10.1001/jamacardio.2025.2522},
journal = {JAMA Cardiology},
pages = {1092--1099},
title = {Artificial Intelligence–enhanced electrocardiography for complete heart block risk stratification},
url = {http://dx.doi.org/10.1001/jamacardio.2025.2522},
volume = {10},
year = {2025}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Introduction Complete heart block (CHB) is a life-threatening condition that can lead to ventricular standstill, syncopal injury, and sudden cardiac death, and current electrocardiography (ECG)-based risk stratification (presence of bifascicular block) is crude and has limited performance. Artificial intelligence–enhanced electrocardiography (AI-ECG) has been shown to identify a broad spectrum of subclinical disease and may be useful for CHB.Objective To develop an AI-ECG risk estimator for CHB (AIRE-CHB) to predict incident CHB.Design, Setting, and Participants This cohort study was a development and external validation prognostic study conducted at Beth Israel Deaconess Medical Center and validated externally in the UK Biobank volunteer cohort.Exposure Electrocardiogram.Main Outcomes and Measures A new diagnosis of CHB more than 31 days after the ECG. AIRE-CHB uses a residual convolutional neural network architecture with a discrete-time survival loss function and was trained to predict incident CHB.Results The Beth Israel Deaconess Medical Center cohort included 1163401 ECGs from 189539 patients. AIRE-CHB predicted incident CHB with a C index of 0.836 (95% CI, 0.819-0.534) and area under the receiver operating characteristics curve (AUROC) for incident CHB within 1 year of 0.889 (95% CI, 0.863-0.916). In comparison, the presence of bifascicular block had an AUROC of 0.594 (95% CI, 0.567-0.620). Participants in the high-risk quartile had an adjusted hazard ratio (aHR) of 11.6 (95% CI, 7.62-17.7; P < .001) for development of incident CHB compared with the low-risk group. In the UKB UK Biobank cohort of 50641 ECGs from 189539 patients, the C index for incident CHB prediction was 0.936 (95% CI, 0.900-0.972) and aHR, 7.17 (95% CI, 1.67-30.81; P < .001).Conclusions and Relevance In this study, a first-of-its-kind deep learning model identified the risk of incident CHB. AIRE-CHB could be used in diverse settings to aid
AU - Sau,A
AU - Zhang,H
AU - Barker,J
AU - Pastika,L
AU - Patlatzoglou,K
AU - Zeidaabadi,B
AU - El-Medany,A
AU - Khattak,GR
AU - McGurk,KA
AU - Sieliwonczyk,E
AU - Ware,JS
AU - Peters,NS
AU - Kramer,DB
AU - Waks,JW
AU - Ng,FS
DO - 10.1001/jamacardio.2025.2522
EP - 1099
PY - 2025///
SN - 2380-6583
SP - 1092
TI - Artificial Intelligence–enhanced electrocardiography for complete heart block risk stratification
T2 - JAMA Cardiology
UR - http://dx.doi.org/10.1001/jamacardio.2025.2522
UR - https://doi.org/10.1001/jamacardio.2025.2522
VL - 10
ER -