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. 2025 Aug 20:e252522.
doi: 10.1001/jamacardio.2025.2522. Online ahead of print.

Artificial Intelligence-Enhanced Electrocardiography for Complete Heart Block Risk Stratification

Arunashis Sau  1   2 , Henry Zhang  1 , Joseph Barker  1 , Libor Pastika  1 , Konstantinos Patlatzoglou  1 , Boroumand Zeidaabadi  1 , Ahmed El-Medany  1 , Gul Rukh Khattak  1 , Kathryn A McGurk  1   3 , Ewa Sieliwonczyk  1   3   4 , James S Ware  1   2   3   5 , Nicholas S Peters  1   2 , Daniel B Kramer  1   6 , Jonathan W Waks  7 , Fu Siong Ng  1   2   8
Affiliations

Artificial Intelligence-Enhanced Electrocardiography for Complete Heart Block Risk Stratification

Arunashis Sau et al. JAMA Cardiol. .

Abstract

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 1 163 401 ECGs from 189 539 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 50 641 ECGs from 189 539 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 in decision-making for individuals with syncope or at risk of high-grade atrioventricular block.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Sau reported a patent for AI-ECG methods pending. Dr Pastika reported a patent for AI methods pending. Dr Zeidaabadi reported a patent for AI-ECG methods pending. Dr El-Medany reported a clinical research fellowship from Chelsea and Westminster NHS Foundation Trust during the conduct of the study. Dr Ware reported consulting for MyoKardia, Pfizer, Foresite Labs, Health Lumen, and Tenaya; research support from Bristol Myers Squibb; and being a founder with equity from Saturnus Bio outside the submitted work. Dr Kramer reported previously serving on the advisory board for HeartcoR Solutions, for whom they remain an independent consultant. Dr Waks reported fees from HeartcoR Solutions for core lab work and as a former member of their advisory board, grants from Anumana for research funding, and consultant fees from Heartbeam outside the submitted work. Dr Ng reported speaking fees from GE Healthcare and consulting fees from Astra-Zeneca during the conduct of the study and having a patent for AI-ECG methods pending. No other disclosures were reported.

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