NCT07038018

Brief Summary

This is a multi-center, retrospective study evaluating the performance of an artificial intelligence-enabled electrocardiography (AI-ECG) algorithm in detecting reduced left ventricular ejection fraction (LVEF ≤ 40%). All included patients from participating hospitals must have undergone a digital 12-lead electrocardiogram (ECG) and an echocardiogram with assessment of LVEF within seven days. The AI-ECG algorithm will be applied to evaluate its diagnostic performance, which will be further assessed across subgroups stratified by demographic characteristics and clinical factors.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
12,500

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Aug 2025

Shorter than P25 for all trials

Geographic Reach
1 country

13 active sites

Status
not yet recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

June 16, 2025

Completed
10 days until next milestone

First Posted

Study publicly available on registry

June 26, 2025

Completed
1 month until next milestone

Study Start

First participant enrolled

August 1, 2025

Completed
1 month until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 31, 2025

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

September 30, 2025

Completed
Last Updated

June 26, 2025

Status Verified

June 1, 2025

Enrollment Period

1 month

First QC Date

June 16, 2025

Last Update Submit

June 25, 2025

Conditions

Keywords

left ventricular dysfunctionartificial intelligence

Outcome Measures

Primary Outcomes (1)

  • The Sensitivity and specificity of AI-ECG model for left ventricular ejection fraction ≤ 40%

    The primary objective of the study was to evaluate the sensitivity and specificity of the artificial intelligence-enabled electrocardiography (AI-ECG) model in detecting left ventricular dysfunction, defined as left ventricular ejection fraction (LVEF) ≤ 40% as confirmed by transthoracic echocardiography.

    within 7 days

Interventions

AI-ECG AlgorithmDIAGNOSTIC_TEST

AI-ECG Algorithm to detect LVEF\<=40%

Eligibility Criteria

Age18 Years - 100 Years
Sexall
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

All patients with ECGs and an echocardiogram within 7 days

You may qualify if:

  • patients with ECGs and an echocardiogram within 7 days

You may not qualify if:

  • Missing ECG signals
  • Missing LVEF assessment in echocardiograms

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (13)

Hualien Armed Forces General Hospital

Hualien City, Taiwan

Location

Kaohsiung Armed Forces General Hospital Gangshan Branch

Kaohsiung City, Taiwan

Location

Kaohsiung Armed Forces General Hospital

Kaohsiung City, Taiwan

Location

Zuoying Armed Forces General Hospital

Kaohsiung City, Taiwan

Location

Tri-Service General Hospital Keelung Branch

Keelung, Taiwan

Location

Tri-Service General Hospital Penghu Branch

Pengfu, Taiwan

Location

Kaohsiung Armed Forces General Hospital Pingtung Branch

Pingtung City, Taiwan

Location

Taichung Armed Forces General Hospital Zhongqing Branch

Taichung, Taiwan

Location

Taichung Armed Forces General Hospital

Taichung, Taiwan

Location

Tri-Service General Hospital Beitou Branch

Taipei, Taiwan

Location

Tri-Service General Hospital Songshan Branch

Taipei, Taiwan

Location

Taoyuan Armed Forces General Hospital Hsinchu Branch

Taoyuan District, Taiwan

Location

Taoyuan Armed Forces General Hospital

Taoyuan District, Taiwan

Location

Related Publications (1)

  • Chen HY, Lin CS, Fang WH, Lou YS, Cheng CC, Lee CC, Lin C. Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis. J Pers Med. 2022 Mar 13;12(3):455. doi: 10.3390/jpm12030455.

    PMID: 35330455BACKGROUND

MeSH Terms

Conditions

Heart DiseasesVentricular Dysfunction, Left

Condition Hierarchy (Ancestors)

Cardiovascular DiseasesVentricular Dysfunction

Central Study Contacts

Wei-Ting Liu, M.D.

CONTACT

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Clinical Doctor, Principal Investigator

Study Record Dates

First Submitted

June 16, 2025

First Posted

June 26, 2025

Study Start

August 1, 2025

Primary Completion

August 31, 2025

Study Completion

September 30, 2025

Last Updated

June 26, 2025

Record last verified: 2025-06

Data Sharing

IPD Sharing
Will not share

Locations