AI-ECG for Time-Resolved Prediction of HFrEF

NCT07519434 | Completed

• 1 other identifier: ZS-HF-AIECG-2026
• Study Type: observational
• Target: 286,709
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to develop and validate a deep learning-based electrocardiogram (ECG) model for predicting the future risk of heart failure with reduced ejection fraction (HFrEF). The model is trained using raw 12-lead ECG data and generates individualized, time-resolved risk estimates over a 5-year period. Data are obtained from multiple cohorts, including Zhongshan Hospital, Shanghai Tenth People's Hospital, and Beth Israel Deaconess Medical Center, representing diverse populations across China and the United States. The model is designed to identify individuals at elevated risk of developing HFrEF before the onset of overt clinical disease. The performance of the model is evaluated using multiple complementary metrics, including discrimination, calibration, and clinical utility. In addition, interpretability analyses are conducted to explore the physiological relevance of ECG features associated with predicted risk. This study seeks to provide an accessible and scalable tool for early risk stratification of heart failure, with the potential to support timely clinical decision-making and improve patient outcomes.

Conditions

Heart Failure; Heart Failure With Reduced Ejection Fraction; Left Ventricular Dysfunction; Cardiac Remodeling

Keywords

Electrocardiogram; Artificial Intelligence; Deep Learning; Survival Model; Time-to-Event Prediction; Risk Stratification; Heart Failure Prediction; ECG-based Prediction

Outcome Measures

Primary Outcomes (1)

• Incident Heart Failure With Reduced Ejection Fraction (HFrEF)

  Occurrence of heart failure with reduced ejection fraction (HFrEF), defined as a left ventricular ejection fraction (LVEF) ≤40% during follow-up, as determined by transthoracic echocardiography. Both prevalent and incident cases identified from ECG-echocardiography data are included.

  Up to 5 years

Study Arms (1)

Overall Study Population

Participants from three independent cohorts (Zhongshan Hospital, Shanghai Tenth People's Hospital, and Beth Israel Deaconess Medical Center) who underwent standard 12-lead electrocardiography and echocardiographic evaluation. These data were used to develop and externally validate a deep learning model for time-to-event prediction of incident heart failure with reduced ejection fraction (HFrEF). No interventions were assigned, as this was an observational study based on routinely collected clinical data.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Participants were derived from three independent cohorts, including Zhongshan Hospital, Shanghai Tenth People's Hospital, and Beth Israel Deaconess Medical Center. The study population included adult patients who underwent routine ECG and echocardiographic evaluation in real-world clinical settings.

You may qualify if:

• 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

You may not qualify if:

• Missing or incomplete ECG or echocardiography data Poor-quality ECG recordings unsuitable for analysis Missing key clinical variables required for model development

Sponsors & Collaborators

• Shanghai Zhongshan Hospital: lead
• Shanghai 10th People's Hospital: collaborator
• Beth Israel Deaconess Medical Center: collaborator

Study Sites (1)

Unknown Facility

Shanghai, Shanghai Municipality, 200436, China

Location

MeSH Terms

Conditions

Heart Failure; Ventricular Dysfunction, Left

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Ventricular Dysfunction

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 2, 2026
• First Posted: April 9, 2026
• Study Start: February 1, 2014
• Primary Completion: December 1, 2023
• Study Completion: December 1, 2023
• Last Updated: April 9, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)