Dhingra Lovedeep S, Aminorroaya Arya, Sangha Veer, Camargos Aline Pedroso, Asselbergs Folkert W, Brant Luisa Cc, Barreto Sandhi M, Ribeiro Antonio Luiz P, Krumholz Harlan M, Oikonomou Evangelos K, Khera Rohan
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Department of Engineering Science, University of Oxford, Oxford, UK.
medRxiv. 2024 Apr 3:2024.04.02.24305232. doi: 10.1101/2024.04.02.24305232.
Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk.
Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk.
Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.
An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.
目前心力衰竭（HF）风险分层策略需要特定的血液生物标志物或全面的临床评估。在本研究中，我们评估了应用于心电图（ECG）图像的人工智能（AI）用于预测HF风险的情况。
在整合的耶鲁纽黑文医疗系统（YNHHS）的跨国纵向队列以及基于人群的英国生物银行（UKB）和巴西成人健康纵向研究（ELSA - Brasil）中，我们确定了基线时无HF的个体。新发HF根据首次HF住院定义。我们评估了一种AI - ECG模型，该模型从12导联ECG的单个图像定义左心室功能障碍的横断面概率及其与新发HF的关联。我们使用Fine - Gray子分布模型考虑死亡的竞争风险，并使用Harrel's c统计量评估辨别力。预防HF的汇总队列方程（PCP - HF）用作估计新发HF风险的对照。
在YNHHS的231,285名个体中，4472人在4.5年（四分位间距2.5 - 6.6）的随访中有首次HF住院。在UKB和ELSA - Brasil中，在42,741人和13,454人中，分别在3.1年（2.1 - 4.5）和4.2年（3.7 - 4.5）的随访中有46人和31人发生HF。AI - ECG筛查阳性预示YNHHS患者发生HF的风险高4倍（年龄、性别调整后的HR [aHR] 3.88 [95% CI，3.63 - 4.14]）。在UKB和ELSA - Brasil中，筛查阳性的ECG分别预示发生HF的风险高13倍和24倍（aHR：UKB，12.85 [6.87 - 24.02]；ELSA - Brasil，23.50 [11.09 - 49.81]）。在考虑合并症和死亡的竞争风险后，这种关联是一致的。较高的模型输出概率与HF风险增加逐渐相关。该模型对YNHHS中新发HF的辨别力为0.718，UKB中为0.769, ELSA - Brasil中为0.810。在所有队列中，将模型概率与PCP - HF相结合比单独使用PCP - HF在辨别力上有显著提高。
应用于12导联ECG图像的AI模型可以识别跨国队列中HF风险升高的个体。作为仅需ECG图像的HF风险数字生物标志物，这种AI - ECG方法可以实现对HF风险的可扩展且高效的筛查。
