Heart FailureNCT07519434
AI-ECG for Time-Resolved Prediction of HFrEF
An observational study of Heart Failure and Heart Failure With Reduced Ejection Fraction, run by Shanghai Zhongshan Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Heart Failure, Heart Failure With Reduced Ejection Fraction, Left Ventricular Dysfunction
- How many people
- 286,709 participants
- Where
- 1 siteChina
- Study dates
- Feb 2014to Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- Missing or incomplete ECG or echocardiography data Poor-quality ECG recordings unsuitable for analysis Missing key clinical variables required for model development
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational Study
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.
Read more
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.
Read the detailed description
Heart failure with reduced ejection fraction (HFrEF) is associated with substantial morbidity and mortality worldwide, and early identification of individuals at risk remains a major clinical challenge. Although existing risk models and biomarkers can provide prognostic information, their application is often limited by the need for laboratory testing or imaging, as well as variability in performance across populations.
In this study, we develop a deep learning-based survival model using raw 12-lead electrocardiogram (ECG) data to predict the future onset of HFrEF. The model is designed to generate individualized, time-to-event risk estimates over a 5-year follow-up period, allowing for dynamic assessment of risk trajectories rather than static classification.
The model is trained on data from Zhongshan Hospital and externally validated in independent cohorts from Shanghai Tenth People's Hospital and Beth Israel Deaconess Medical Center. These cohorts include a broad spectrum of patients, ranging from individuals without known cardiovascular disease to those with diverse clinical conditions, thereby enabling evaluation of model generalizability across different healthcare systems and demographic subgroups.
Model performance is comprehensively assessed using multiple metrics, including the concordance index, time-dependent area under the receiver operating characteristic curve, area under the precision-recall curve, Brier score, calibration analysis, and decision curve analysis. Risk stratification capability is evaluated using Kaplan-Meier survival analysis.
To enhance interpretability, complementary representation-based and attention-based methods are applied. These include variational autoencoder-derived latent feature analysis, correlation with conventional ECG parameters, and gradient-based visualization techniques to identify waveform regions contributing to model predictions. These approaches aim to ensure that the model captures physiologically meaningful signals associated with myocardial remodeling and cardiac dysfunction.
This study is observational and retrospective in nature and does not involve any intervention. The findings aim to support the development of a non-invasive, cost-effective, and widely accessible tool for early detection of individuals at risk of HFrEF, with potential implications for preventive strategies and personalized clinical management.
- 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
- Medical subject headings
- Heart Failure, Ventricular Dysfunction, Left
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Up to 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Apr 2026
- Study startFeb 2014
- Primary completionDec 2023
- Study completionDec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart FailureStudies of the same condition, open and under way.
- More trials run by Shanghai Zhongshan HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 286,709 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shanghai Zhongshan Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai 10th People's Hospital, Beth Israel Deaconess Medical Center
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07519434
- Sponsor's protocol ID
- ZS-HF-AIECG-2026
- Status last verified
- Apr 2026
- First submitted
- 2 Apr 2026
- Last updated
- 9 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This study uses retrospective clinical data that are not publicly shareable.
Study at a glance
From the registry record, last updated by the sponsor on 9 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational Study
- ClinicalTrials.gov ID
- NCT07519434
- Acronym / sponsor ID
- ZS-HF-AIECG-2026
- 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
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 286,709 participants (actual)
- Lead sponsor
- Shanghai Zhongshan HospitalUniversity, hospital or other
- Collaborators
- Shanghai 10th People's Hospital, Beth Israel Deaconess Medical Center
- Study start
- Feb 2014 (actual)
- Primary completion
- Dec 2023 (actual)
- Study completion
- Dec 2023 (actual)
- First posted
- 9 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Heart failure with reduced ejection fraction (HFrEF) is associated with substantial morbidity and mortality worldwide, and early identification of individuals at risk remains a major clinical challenge. Although existing risk models and biomarkers can provide prognostic information, their application is often limited by the need for laboratory testing or imaging, as well as variability in performance across populations.
In this study, we develop a deep learning-based survival model using raw 12-lead electrocardiogram (ECG) data to predict the future onset of HFrEF. The model is designed to generate individualized, time-to-event risk estimates over a 5-year follow-up period, allowing for dynamic assessment of risk trajectories rather than static classification.
The model is trained on data from Zhongshan Hospital and externally validated in independent cohorts from Shanghai Tenth People's Hospital and Beth Israel Deaconess Medical Center. These cohorts include a broad spectrum of patients, ranging from individuals without known cardiovascular disease to those with diverse clinical conditions, thereby enabling evaluation of model generalizability across different healthcare systems and demographic subgroups.
Model performance is comprehensively assessed using multiple metrics, including the concordance index, time-dependent area under the receiver operating characteristic curve, area under the precision-recall curve, Brier score, calibration analysis, and decision curve analysis. Risk stratification capability is evaluated using Kaplan-Meier survival analysis.
To enhance interpretability, complementary representation-based and attention-based methods are applied. These include variational autoencoder-derived latent feature analysis, correlation with conventional ECG parameters, and gradient-based visualization techniques to identify waveform regions contributing to model predictions. These approaches aim to ensure that the model captures physiologically meaningful signals associated with myocardial remodeling and cardiac dysfunction.
This study is observational and retrospective in nature and does not involve any intervention. The findings aim to support the development of a non-invasive, cost-effective, and widely accessible tool for early detection of individuals at risk of HFrEF, with potential implications for preventive strategies and personalized clinical management.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- 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
Key exclusion criteria (1 listed)
- Missing or incomplete ECG or echocardiography data Poor-quality ECG recordings unsuitable for analysis Missing key clinical variables required for model development
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Incident Heart Failure With Reduced Ejection Fraction (HFrEF)
Time frame: Up to 5 years
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Apr 2026.
- Individual participant dataSharing planned: No. This study uses retrospective clinical data that are not publicly shareable.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure and Heart Failure With Reduced Ejection Fraction, run by Shanghai Zhongshan Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incident Heart Failure With Reduced Ejection Fraction (HFrEF) (time frame: Up to 5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Apr 2026.