Croon Philip M, Dhingra Lovedeep S, Biswas Dhruva, Oikonomou Evangelos K, Khera Rohan
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT.
Circulation. 2025 Sep 1. doi: 10.1161/CIRCULATIONAHA.125.076279.
Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their clinical use. Here, we sought to assess whether AI-ECG models function as condition-specific classifiers or broader cardiovascular risk markers.
We included four distinct study populations, drawn from both electronic health records (EHR) and prospective cohort studies. We deployed six image-based AI-ECG models, including five validated models for the detection of left ventricular systolic dysfunction (LVSD), aortic stenosis (AS), mitral regurgitation (MR), left ventricular hypertrophy (LVH), a composite model for structural heart disease (SHD), and a negative control AI-ECG model for biological sex. Additionally, we developed six experimental models designed to identify non-cardiovascular conditions. Diagnosis codes from EHR and cohorts were transformed into interpretable phenotypes using a phenome-wide association study (PheWAS) framework. We assessed associations of AI-ECG probabilities with cross-sectional phenotypes using logistic regression, and with new-onset cardiovascular diseases using Cox regression. Pearson correlation coefficients were calculated to compare phenotypic signatures.
The study included one random ECG from 233,689 individuals (mean age 59±18 years, 130,084 [56%] women) across sites. Each of the five AI-ECG models was more likely to be associated with cardiovascular phenotypes compared with other phenotype groups (odds ratios ranging from 2.16 to 4.41, p<10), whereas the sex model did not show a similar pattern. All AI-ECG models were significantly associated with their respective target phenotype but also showed similar or stronger associations with a broad range of other cardiovascular phenotypes. Phenotypic associations were similar across AI-ECG models trained for different conditions, which was not observed in models for non-cardiovascular conditions. Correlation of phenotype association patterns between models was high (0.67 to 0.96). This pattern was consistent across all models, external datasets, and in both cross-sectional and prospective analyses.
Despite being developed to detect specific cardiovascular conditions, AI-ECG models detect the presence and predict the future development of a broad range of cardiovascular diseases with similar propensity. This challenges their role as binary diagnostic tools and instead supports their use as broader cardiovascular biomarkers.
人工智能（AI）增强型心电图（ECG）模型通常旨在检测特定的心脏解剖和功能异常。了解其表型关联的选择性对于指导其临床应用至关重要。在此，我们试图评估AI-ECG模型是作为特定疾病分类器还是更广泛的心血管风险标志物发挥作用。
我们纳入了来自电子健康记录（EHR）和前瞻性队列研究的四个不同研究人群。我们部署了六个基于图像的AI-ECG模型，包括五个用于检测左心室收缩功能障碍（LVSD）、主动脉瓣狭窄（AS）、二尖瓣反流（MR）、左心室肥厚（LVH）的验证模型、一个结构性心脏病（SHD）复合模型以及一个用于生物性别的阴性对照AI-ECG模型。此外，我们开发了六个旨在识别非心血管疾病的实验模型。使用全表型关联研究（PheWAS）框架将EHR和队列中的诊断代码转换为可解释的表型。我们使用逻辑回归评估AI-ECG概率与横断面表型的关联，使用Cox回归评估与新发心血管疾病的关联。计算Pearson相关系数以比较表型特征。
该研究纳入了来自各研究点的233,689名个体（平均年龄59±18岁，130,084名[56%]为女性）的一份随机心电图。与其他表型组相比，五个AI-ECG模型中的每一个与心血管表型的关联可能性更高（比值比范围为2.16至4.41，p<10），而性别模型未显示出类似模式。所有AI-ECG模型均与其各自的目标表型显著相关，但也显示出与广泛的其他心血管表型相似或更强的关联。针对不同疾病训练的AI-ECG模型的表型关联相似，而在非心血管疾病模型中未观察到这种情况。模型之间表型关联模式的相关性很高（0.67至0.96）。这种模式在所有模型、外部数据集以及横断面和前瞻性分析中均一致。
尽管AI-ECG模型是为检测特定心血管疾病而开发的，但它们以相似的倾向检测广泛心血管疾病的存在并预测其未来发展。这对它们作为二元诊断工具的作用提出了挑战，相反，支持它们作为更广泛的心血管生物标志物的用途。
