ArticleJAMA cardiology2025
Artificial Intelligence-Enhanced Electrocardiography for Complete Heart Block Risk Stratification.
Article in JAMA cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Artificial intelligence for patient selection in pulsed-field ablation: promise, pragmatism, and the need for standards.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
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Authors and funding
15 authors.
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No grant is acknowledged in the PubMed record.
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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