Prospective Validation of a Deep Learning Model to Detect Structural Heart Disease from Apple Watch ECGs: The WATCH-SHD Study

Importance

Consumer wearables such as the Apple Watch can record single-lead electrocardiograms (ECGs) but are used mainly to detect rhythm disorders. Artificial intelligence–enhanced ECG (AI-ECG) could extend these real-world recordings for detecting structural heart disease (SHD), yet prospective validation remains limited.

Objective

To prospectively validate a previously developed, noise-adapted AI-ECG model for detecting severe SHD from single-lead Apple Watch ECGs.

Design

Prospective cohort study.

Setting

Yale New Haven Hospital echocardiography laboratory.

Participants

Adults aged ≥18 years undergoing outpatient transthoracic echocardiography (TTE) as part of routine clinical care.

Exposure

A 30-second, single-lead Apple Watch ECG recorded during the TTE visit and processed through an end-to-end, HIPAA-compliant platform for real-time AI-ECG inference.

Main Outcomes and Measures

The primary outcome was discrimination for TTE-defined severe SHD—a composite of left ventricular systolic dysfunction (left ventricular ejection fraction <40%), severe left-sided valvular disease, and/or severe left ventricular hypertrophy—assessed by the area under the receiver operating characteristic curve (AUROC). Secondary measures were sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) at prespecified thresholds, and screening efficiency, assessed by the number needed to test (NNT) under usual-care versus AI-ECG–guided strategies.

Results

Among 596 participants with analyzable Apple Watch ECGs (median age, 62 years [IQR, 46–72]; 51.2% women), severe SHD was present in 30 (5.1%). The model discriminated severe SHD well (AUROC, 0.841; 95% CI, 0.761–0.921), with a sensitivity of 76.7% (59.1–88.2), specificity of 83.2% (79.9–86.1), NPV of 98.5% (97.0–99.3), and PPV of 19.7% (13.5–27.8) at the prespecified threshold. An AI-ECG–guided strategy reduced the NNT to identify one case by more than 60% versus usual care across the composite and individual SHD phenotypes.

Conclusions and Relevance

In this prospective cohort, a noise-adapted AI-ECG algorithm identified SHD phenotypes from real-world single-lead Apple Watch ECGs and improved screening efficiency. These findings support a potential role for wearable ECG–based screening in the scalable identification of clinically actionable SHD.

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