CompletedNCT07519434

Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational Study

Sponsor: Shanghai Zhongshan Hospital

Trial Details

Condition	Heart Failure, Heart Failure With Reduced Ejection Fraction, Left Ventricular Dysfunction, Cardiac Remodeling
Status	Completed
Participants	286,709
Est. Completion	1 Dec 2023
Last Updated	9 Apr 2026

Primary Outcome

Incident Heart Failure With Reduced Ejection Fraction (HFrEF)

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: Adults aged ≥18 years Underwent standard 12-lead electrocardiography (ECG) Underwent transthoracic echocardiography with available LVEF measurement Availability of paired ECG-echocardiography data Data available for follow-up assessment Exclusion Criteria: Missing or incomplete ECG or echocardiography data Poor-quality ECG recordings unsuitable for analysis Missing key clinical variables required for model development

Trial Sites

Countries

China

View Registry Entry

Related Analysis

Major Deep Learning Study Validates ECG-Based Prediction of Heart Failure With Reduced Ejection Fraction — Clinical Implications for Early Detection

A major multinational study of 286,709 patients validates deep learning algorithms using standard 12-lead ECGs to predict heart failure with reduced ejection fraction up to 5 years before clinical onset. This breakthrough could transform cardiovascular prevention strategies using readily available diagnostic tools.

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