Kany Shinwan, Friedman Samuel F, Al-Alusi Mostafa, Khurshid Shaan, Rämö Joel T, Pipilas Daniel, Pirruccello James P, Reeder Christopher, Philippakis Anthony A, Ho Jennifer E, Maddah Mahnaz, Ellinor Patrick T, Fahed Akl C
Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany.
Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
JACC Adv. 2025 Jul 31;4(9):102041. doi: 10.1016/j.jacadv.2025.102041.
Coronary artery disease (CAD) results in substantial morbidity and mortality.
The purpose of this study was to develop a deep learning model to detect CAD defined using diagnostic codes ("ECG2CAD") and identify people at risk for adverse events using electrocardiograms (ECGs) in a primary care setting.
ECG2CAD was trained on 764,670 ECGs representing 137,199 individuals at Massachusetts General Hospital (MGH). Model performance for discrimination of prevalent CAD was measured using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), and compared against model of age and sex, and Pooled Cohort Equations, in 3 test sets: MGH, Brigham and Women's Hospital (BWH), and UK Biobank. Subgroups were assessed for incident CAD-related events in a BWH primary care cohort.
ECG2CAD was evaluated in MGH (N = 18,706 [6,051 cases], age 57 ± 16 years), BWH (N = 88,270 [27,898 cases], age 57 ± 16 years), and UK Biobank (N = 42,147 [1,509 cases], age 65 ± 8 years). ECG2CAD consistently discriminated prevalent CAD (MGH AUROC: 0.782; AUPRC: 0.639; BWH: AUROC: 0.747; AUPRC: 0.588; UK Biobank AUROC: 0.760; AUPRC: 0.155) and incrementally vs models based on age and sex or Pooled Cohort Equations (P < 0.01) in MGH and BWH. In the BWH primary care subset, model performance was consistent across subgroups. Being in the highest quintile of ECG2CAD risk was associated with higher risk for adverse events compared with low-risk group (myocardial infarction HR: 5.59; 95% CI: 4.76-6.56, heart failure 10.49; 95% CI: 7.96-13.84, all-cause mortality 2.68; 95% CI: 2.32-3.10).
Artificial intelligence-enabled analysis of the ECG may facilitate identification of individuals with possible undiagnosed CAD and inform downstream testing and preventive measures.
冠状动脉疾病（CAD）会导致严重的发病和死亡。
本研究的目的是开发一种深度学习模型，以检测使用诊断代码定义的CAD（“ECG2CAD”），并在初级保健环境中使用心电图（ECG）识别有不良事件风险的人群。
ECG2CAD在代表马萨诸塞州总医院（MGH）137,199名个体的764,670份心电图上进行训练。在3个测试集（MGH、布莱根妇女医院（BWH）和英国生物银行）中，使用受试者工作特征曲线下面积（AUROC）和精确召回率曲线下面积（AUPRC）测量区分 prevalent CAD的模型性能，并与年龄和性别模型以及汇总队列方程进行比较。在BWH初级保健队列中评估亚组的CAD相关事件发生率。
在MGH（N = 18,706 [6,051例]，年龄57±16岁）、BWH（N = 88,270 [27,898例]，年龄57±16岁）和英国生物银行（N = 42,147 [1,509例]，年龄65±8岁）中对ECG2CAD进行了评估。ECG2CAD始终能够区分 prevalent CAD（MGH的AUROC：0.782；AUPRC：0.639；BWH的AUROC：0.747；AUPRC：0.588；英国生物银行的AUROC：0.760；AUPRC：0.155），并且在MGH和BWH中与基于年龄和性别的模型或汇总队列方程相比有增量改善（P < 0.01）。在BWH初级保健子集中，模型性能在各亚组中保持一致。与低风险组相比，处于ECG2CAD风险最高五分位数的人群发生不良事件的风险更高（心肌梗死HR：5.59；95% CI：4.76 - 6.56，心力衰竭10.49；95% CI：7.96 - 13.84，全因死亡率2.68；95% CI：2.32 - 3.10）。
基于人工智能的心电图分析可能有助于识别可能未被诊断的CAD个体，并为下游检测和预防措施提供信息。
