AI-ECG for One-Year Mortality Risk Prediction

NCT07659262 | Completed

• 1 other identifier: A202503002
• Study Type: observational
• Target: 461,982
• Locations: 1 country
• Sites: 3

Brief 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 (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.

Conditions

Electrocardiogram; Mortality Risk Prediction

Outcome Measures

Primary Outcomes (1)

• 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.

  Up to 1 year (365 days) from the index ECG examination.

Interventions

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.

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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..

Sponsors & Collaborators

• National Defense Medical Center, Taiwan: lead
• Kaohsiung Armed Forces General Hospital: collaborator
• Taipei Medical University WanFang Hospital: collaborator

Study Sites (3)

Kaohsiung Armed Forces General Hospital

Kaohsiung City, 807, Taiwan

Location

Taipei Municipal Wanfang Hospital

Taipei, 114, Taiwan

Location

Tri-Service General Hospital

Taipei, 114, Taiwan

Location

Study Officials

• Chin Lin, PhD

  National Defense Medical Center

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 15, 2026
• First Posted: June 22, 2026
• Study Start: April 1, 2025
• Primary Completion: July 21, 2025
• Study Completion: July 21, 2025
• Last Updated: June 24, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

TW (3)