Sau Arunashis, Zhang Henry, Barker Joseph, Pastika Libor, Patlatzoglou Konstantinos, Zeidaabadi Boroumand, El-Medany Ahmed, Khattak Gul Rukh, McGurk Kathryn A, Sieliwonczyk Ewa, Ware James S, Peters Nicholas S, Kramer Daniel B, Waks Jonathan W, Ng Fu Siong
National Heart and Lung Institute, Imperial College London, London, United Kingdom.
Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom.
JAMA Cardiol. 2025 Aug 20. doi: 10.1001/jamacardio.2025.2522.
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.
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.
Electrocardiogram.
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.
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).
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.
完全性心脏传导阻滞（CHB）是一种危及生命的疾病，可导致心室停搏、晕厥损伤和心源性猝死，而目前基于心电图（ECG）的风险分层（存在双分支阻滞）较为粗略，性能有限。人工智能增强心电图（AI-ECG）已被证明可识别多种亚临床疾病，可能对CHB有用。
开发一种用于CHB的AI-ECG风险评估器（AIRE-CHB）以预测CHB的发生。
设计、设置和参与者：这项队列研究是在贝斯以色列女执事医疗中心进行的一项开发和外部验证的预后研究，并在英国生物银行志愿者队列中进行了外部验证。
心电图。
心电图检查后31天以上新诊断的CHB。AIRE-CHB使用具有离散时间生存损失函数的残差卷积神经网络架构，并经过训练以预测CHB的发生。
贝斯以色列女执事医疗中心队列包括来自189539名患者的1163401份心电图。AIRE-CHB预测CHB发生的C指数为0.836（95%CI，0.819-0.854），1年内CHB发生的受试者工作特征曲线下面积（AUROC）为0.889（95%CI，0.863-0.916）。相比之下，双分支阻滞的存在其AUROC为0.594（95%CI，0.567-0.620）。与低风险组相比，高风险四分位数的参与者发生CHB的调整后风险比（aHR）为11.6（95%CI，7.62-17.7；P < .001）。在英国生物银行队列的189539名患者的50641份心电图中，预测CHB发生的C指数为0.936（95%CI，0.900-0.972），aHR为7.17（95%CI，1.67-30.81；P < .001）。
在本研究中，一种首创的深度学习模型识别出了CHB发生的风险。AIRE-CHB可用于不同场景，以辅助对晕厥患者或有高度房室传导阻滞风险的个体进行决策。
