ElectrocardiogramNCT07659262
AI-ECG for One-Year Mortality Risk Prediction
An observational study of Electrocardiogram and Mortality Risk Prediction, run by National Defense Medical Center, Taiwan at 3 sites in Taiwan.
Trial summary
Cardiovascular disease (CVD) remains one of the leading causes of death worldwide. While the electrocardiogram (ECG) is a standard, widely accessible tool for cardiovascular screening, traditional risk assessment models often rely heavily on blood test results, which may be unavailable in electronic health records… Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Electrocardiogram, Mortality Risk Prediction
- What is being tested?
- Chang Gung ECG Mortality Risk Prediction Software (Model: CGMH-DRP-001)
- Type of treatment
- Medical device
- How many people
- 461,982 participants
- Where
- 3 sitesTaiwan
- Study dates
- 1 Apr 2025to 21 Jul 2025
- How long will I take part?
- 1 Year
- 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 20 years and older
- All sexes
- Adult, Older adult
Study population: The study population consists of adult patients (aged 20 years and older) with suspected cardiovascular disease who underwent standard 12-lead resting electrocardiogram (ECG) examinations. Data will be retrospectively collected from three medical institutions in Taiwan-Tri-Service General Hospital, Kaohsiung Armed Forces General Hospital, and Taipei Municipal Wanfang Hospital-between August 2011 and September 2024. The study population represents a diverse real-world patient population across multiple clinical settings, including outpatient clinics, inpatient wards, and emergency departments, with comprehensive documentation of clinical diagnoses and one-year mortality outcomes.
Who may be able to join
- Adults aged 20 years and older.
- Patients who underwent a 12-lead resting electrocardiogram (ECG).
- ECG records must meet the software input specifications: 12 leads, a sampling rate of 500 Hz, a 60-Hz Alternating Current (AC) filter, a recording duration of 10 seconds, and Extensible Markup Language (XML) file format.
- Only the first eligible 12-lead ECG record from each patient will be included to avoid intra-individual bias.
Who cannot join
- ECG records with missing leads.
- Cases with missing demographic information (e.g., age, sex, or mortality status) or incomplete clinical diagnostic data.
- ECG records that do not meet the software input specifications (e.g., an incorrect sampling rate, AC filter setting, recording duration, or file format).
- Pregnant women and patients with implanted pacemakers..
Trial locations and contacts
3 sites in 1 country.
Taiwan3 sites
Kaohsiung Armed Forces General Hospital
Kaohsiung City, 807
Taipei Municipal Wanfang Hospital
Taipei, 114
Tri-Service General Hospital
Taipei, 114
About this study
Official title: An Artificial Intelligence-Based Electrocardiogram Analysis System for One-Year Mortality Risk Prediction
Cardiovascular disease (CVD) remains one of the leading causes of death worldwide. While the electrocardiogram (ECG) is a standard, widely accessible tool for cardiovascular screening, traditional risk assessment models often rely heavily on blood test results, which may be unavailable in electronic health records (EHRs). To address this limitation, the Chang Gung ECG Mortality Risk Prediction Software, an artificial intelligence (AI)-based Software as a Medical Device (SaMD), was developed. The software analyzes standard 10-second, 12-lead resting ECG signals to predict the probability of cardiac-related mortality within one year.
This study is a multicenter retrospective cohort study designed to validate the clinical performance of the AI software. Researchers will analyze retrospectively collected ECG data from patients aged 20 years or older with suspected cardiovascular disease across three hospitals in Taiwan. The AI model's predictions will be compared with the actual one-year mortality outcomes documented in the patients' medical records. The primary objective is to determine whether the AI model can accurately and consistently stratify patients according to their risk of cardiac-related mortality (e.g., heart failure, arrhythmia, and myocardial infarction), with an area under the receiver operating characteristic curve (AUC) greater than 0.80. The software is intended to serve as a clinical decision-support tool for long-term risk stratification in non-acute clinical settings, thereby assisting physicians in clinical decision-making and long-term patient management.
Read the detailed description
Cardiovascular disease (CVD) is a major global health burden. Current cardiovascular risk assessment models (e.g., Framingham and QRISK) rely heavily on blood biochemical data, which limits their applicability when electronic health record (EHR) data are incomplete. The 12-lead resting electrocardiogram (ECG) is a rapid, non-invasive, and widely accessible screening tool. Recent advances in artificial intelligence (AI), particularly deep learning models such as ResNet, have demonstrated superior capabilities for automatically extracting clinically relevant features from ECG signals to predict cardiovascular risk.
This national multi-center retrospective study aims to evaluate the clinical performance of the Chang Gung ECG Mortality Risk Prediction Software, a standalone Software as a Medical Device (SaMD). The core algorithm utilizes a 1D-ResNet-18 convolutional neural network to analyze 10-second, 12-lead resting ECG signals sampled at 500 Hz with a 60-Hz Alternating Current (AC) filter. The software outputs the predicted probability of cardiac-related mortality within one year to assist physicians in non-acute clinical settings.
Study Methodology: The study will retrospectively collect de-identified electronic health records and ECG data obtained between August 2011 and September 2024 from three institutions in Taiwan: Tri-Service General Hospital, Kaohsiung Armed Forces General Hospital, and Taipei Municipal Wanfang Hospital. Only the first eligible ECG from each patient will be included to avoid intra-individual bias.
The AI model's predictions will be compared with the actual one-year mortality outcomes. To improve interpretability, cardiologists with more than five years of clinical experience will review high-risk predictions using Gradient-weighted Class Activation Mapping (Grad-CAM). A prediction will be considered clinically interpretable only if both the predicted risk and the corresponding Grad-CAM localization are deemed clinically reasonable.
Statistical Analysis: The study employs a one-tailed superiority design with a significance level of 0.05. The null hypothesis states that the area under the receiver operating characteristic curve (AUC) is ≤0.80, whereas the alternative hypothesis states that the AUC is >0.80. Subgroup analyses will be performed according to age groups (e.g., 20-40, 41-60, and >60 years) and disease categories (e.g., arrhythmia, myocardial infarction, and heart failure). DeLong's test will be used to evaluate the model's predictive performance across different demographic and clinical subgroups.
Data Privacy and Federated Learning: All patient data will be strictly de-identified in accordance with Health Insurance Accountability and Portability Act (HIPAA) guidelines and analyzed within secure, closed intra-hospital networks. If the initial validation does not achieve the predefined performance target (AUC >0.80), a horizontal federated learning architecture will be implemented. Up to 10% of the available dataset will be used for model fine-tuning, after which the updated model will be independently validated using a separate untouched dataset to ensure robustness and prevent data contamination.
- Conditions
- Electrocardiogram, Mortality Risk Prediction
- Being tested
- Chang Gung ECG Mortality Risk Prediction Software (Model: CGMH-DRP-001)
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Chang Gung ECG Mortality Risk Prediction Software (Model: CGMH-DRP-001)Device
A stand-alone 1D-ResNet-18 deep learning software that analyzes 10-second, 12-lead resting ECG signals to predict the one-year mortality risk associated with cardiac diseases.
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve (AUC) for Predicting One-Year Cardiac-Related Mortality
The primary outcome measure is the area under the receiver operating characteristic curve (AUC) for predicting one-year cardiac-related mortality. The AI model's predictions will be retrospectively compared with the actual one-year mortality outcomes documented in electronic health records (EHRs) and the death registry. The study will be considered successful if the observed AUC is greater than 0.80.
Measured: Up to 1 year (365 days) from the index ECG examination.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jun 2026
- Study start1 Apr 2025
- Primary completion21 Jul 2025
- Study completion21 Jul 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ElectrocardiogramStudies of the same condition, open and under way.
- 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
- Other
- Enrolment
- 461,982 participants (actual)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- National Defense Medical Center, Taiwan
- Sponsor type
- University, hospital or other
- Collaborators
- Kaohsiung Armed Forces General Hospital, Taipei Medical University WanFang Hospital
- Responsible party
- Principal investigator, Chin Lin, National Defense Medical Center, Taiwan
- Investigators
- Chin Lin, PhD, Principal investigator, National Defense Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07659262
- Sponsor's protocol ID
- A202503002
- Status last verified
- Jun 2026
- First submitted
- 15 Jun 2026
- Last updated
- 24 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Artificial Intelligence-Based Electrocardiogram Analysis System for One-Year Mortality Risk Prediction
- ClinicalTrials.gov ID
- NCT07659262
- Acronym / sponsor ID
- A202503002
- Conditions
- Electrocardiogram, Mortality Risk Prediction
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 461,982 participants (actual)
- Lead sponsor
- National Defense Medical Center, TaiwanUniversity, hospital or other
- Collaborators
- Kaohsiung Armed Forces General Hospital, Taipei Medical University WanFang Hospital
- Study start
- 1 Apr 2025 (actual)
- Primary completion
- 21 Jul 2025 (actual)
- Study completion
- 21 Jul 2025 (actual)
- First posted
- 22 Jun 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.
Cardiovascular disease (CVD) remains one of the leading causes of death worldwide. While the electrocardiogram (ECG) is a standard, widely accessible tool for cardiovascular screening, traditional risk assessment models often rely heavily on blood test results, which may be unavailable in electronic health records (EHRs). To address this limitation, the Chang Gung ECG Mortality Risk Prediction Software, an artificial intelligence (AI)-based Software as a Medical Device (SaMD), was developed. The software analyzes standard 10-second, 12-lead resting ECG signals to predict the probability of cardiac-related mortality within one year.
This study is a multicenter retrospective cohort study designed to validate the clinical performance of the AI software. Researchers will analyze retrospectively collected ECG data from patients aged 20 years or older with suspected cardiovascular disease across three hospitals in Taiwan. The AI model's predictions will be compared with the actual one-year mortality outcomes documented in the patients' medical records. The primary objective is to determine whether the AI model can accurately and consistently stratify patients according to their risk of cardiac-related mortality (e.g., heart failure, arrhythmia, and myocardial infarction), with an area under the receiver operating characteristic curve (AUC) greater than 0.80. The software is intended to serve as a clinical decision-support tool for long-term risk stratification in non-acute clinical settings, thereby assisting physicians in clinical decision-making and long-term patient management.
Detailed description
Cardiovascular disease (CVD) is a major global health burden. Current cardiovascular risk assessment models (e.g., Framingham and QRISK) rely heavily on blood biochemical data, which limits their applicability when electronic health record (EHR) data are incomplete. The 12-lead resting electrocardiogram (ECG) is a rapid, non-invasive, and widely accessible screening tool. Recent advances in artificial intelligence (AI), particularly deep learning models such as ResNet, have demonstrated superior capabilities for automatically extracting clinically relevant features from ECG signals to predict cardiovascular risk.
This national multi-center retrospective study aims to evaluate the clinical performance of the Chang Gung ECG Mortality Risk Prediction Software, a standalone Software as a Medical Device (SaMD). The core algorithm utilizes a 1D-ResNet-18 convolutional neural network to analyze 10-second, 12-lead resting ECG signals sampled at 500 Hz with a 60-Hz Alternating Current (AC) filter. The software outputs the predicted probability of cardiac-related mortality within one year to assist physicians in non-acute clinical settings.
Study Methodology: The study will retrospectively collect de-identified electronic health records and ECG data obtained between August 2011 and September 2024 from three institutions in Taiwan: Tri-Service General Hospital, Kaohsiung Armed Forces General Hospital, and Taipei Municipal Wanfang Hospital. Only the first eligible ECG from each patient will be included to avoid intra-individual bias.
The AI model's predictions will be compared with the actual one-year mortality outcomes. To improve interpretability, cardiologists with more than five years of clinical experience will review high-risk predictions using Gradient-weighted Class Activation Mapping (Grad-CAM). A prediction will be considered clinically interpretable only if both the predicted risk and the corresponding Grad-CAM localization are deemed clinically reasonable.
Statistical Analysis: The study employs a one-tailed superiority design with a significance level of 0.05. The null hypothesis states that the area under the receiver operating characteristic curve (AUC) is ≤0.80, whereas the alternative hypothesis states that the AUC is >0.80. Subgroup analyses will be performed according to age groups (e.g., 20-40, 41-60, and >60 years) and disease categories (e.g., arrhythmia, myocardial infarction, and heart failure). DeLong's test will be used to evaluate the model's predictive performance across different demographic and clinical subgroups.
Data Privacy and Federated Learning: All patient data will be strictly de-identified in accordance with Health Insurance Accountability and Portability Act (HIPAA) guidelines and analyzed within secure, closed intra-hospital networks. If the initial validation does not achieve the predefined performance target (AUC >0.80), a horizontal federated learning architecture will be implemented. Up to 10% of the available dataset will be used for model fine-tuning, after which the updated model will be independently validated using a separate untouched dataset to ensure robustness and prevent data contamination.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Year
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients (aged 20 years and older) with suspected cardiovascular disease who underwent standard 12-lead resting electrocardiogram (ECG) examinations. Data will be retrospectively collected from three medical institutions in Taiwan-Tri-Service General Hospital, Kaohsiung Armed Forces General Hospital, and Taipei Municipal Wanfang Hospital-between August 2011 and September 2024. The study population represents a diverse real-world patient population across multiple clinical settings, including outpatient clinics, inpatient wards, and emergency departments, with comprehensive documentation of clinical diagnoses and one-year mortality outcomes.
Key inclusion criteria (4 listed)
- Adults aged 20 years and older.
- Patients who underwent a 12-lead resting electrocardiogram (ECG).
- ECG records must meet the software input specifications: 12 leads, a sampling rate of 500 Hz, a 60-Hz Alternating Current (AC) filter, a recording duration of 10 seconds, and Extensible Markup Language (XML) file format.
- Only the first eligible 12-lead ECG record from each patient will be included to avoid intra-individual bias.
Key exclusion criteria (4 listed)
- ECG records with missing leads.
- Cases with missing demographic information (e.g., age, sex, or mortality status) or incomplete clinical diagnostic data.
- ECG records that do not meet the software input specifications (e.g., an incorrect sampling rate, AC filter setting, recording duration, or file format).
- Pregnant women and patients with implanted pacemakers..
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Chang Gung ECG Mortality Risk Prediction Software (Model: CGMH-DRP-001)A stand-alone 1D-ResNet-18 deep learning software that analyzes 10-second, 12-lead resting ECG signals to predict the one-year mortality risk associated with cardiac diseases. | Device | – |
Primary outcome
Area Under the Receiver Operating Characteristic Curve (AUC) for Predicting One-Year Cardiac-Related Mortality
Time frame: Up to 1 year (365 days) from the index ECG examination.
The primary outcome measure is the area under the receiver operating characteristic curve (AUC) for predicting one-year cardiac-related mortality. The AI model's predictions will be retrospectively compared with the actual one-year mortality outcomes documented in electronic health records (EHRs) and the death registry. The study will be considered successful if the observed AUC is greater than 0.80.
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 24 Jun 2026.
- Individual participant dataSharing planned: No
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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.
Chin Lin, PhD principal investigator
National Defense Medical Center
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Electrocardiogram and Mortality Risk Prediction, run by National Defense Medical Center, Taiwan at 3 sites in Taiwan.
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 4 inclusion and 4 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 24 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Taiwan. 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?
Area Under the Receiver Operating Characteristic Curve (AUC) for Predicting One-Year Cardiac-Related Mortality (time frame: Up to 1 year (365 days) from the index ECG examination.). 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 24 Jun 2026.