External Validation of Artificial Intelligence-enabled Electrocardiography (AI-ECG) for the Detection of Left Ventricular Dysfunction (LVD)

NCT ID: NCT07038018

Last Updated: 2025-06-26

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 12500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-08-01
• Study Completion Date: 2025-09-30

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.

Detailed Description

Data were collected from 13 hospitals, excluding the medical center that developed the artificial intelligence-enabled electrocardiography (AI-ECG) algorithm. The primary objective of the study was to evaluate the sensitivity and specificity of the AI-ECG model in detecting left ventricular dysfunction, defined as left ventricular ejection fraction (LVEF) ≤ 40%. To ensure clinical applicability, predefined thresholds required both sensitivity and specificity to exceed 0.80 in external validation cohorts. Sample size calculations were based on testing the null hypothesis that sensitivity equals 0.80. In the development hospital cohort, the model demonstrated a sensitivity of 0.869 and a specificity of 0.896. With a two-sided significance level (α) of 0.05 and a power of 90%, an estimated 310 cases of LVEF ≤ 40% were required.

Given that the prevalence of left ventricular dysfunction was 4% in the development hospital cohort but expected to be lower-between 2.5% and 3%-in external validation settings (i.e., regional and local hospitals), the total sample size needed to accrue the target number of cases was estimated to range between 10,333 and 12,400 patients. To achieve this, six regional hospitals and seven local hospitals were selected as external validation sites. Because both electrocardiography and echocardiography were required within a seven-day interval-leading to anticipated exclusions-approximately 1,500 patients were targeted from each regional hospital and 500 from each local hospital, resulting in a final target sample size of approximately 12,500 patients.

Conditions

Cardiac Disease

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

AI-ECG Algorithm

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• patients with ECGs and an echocardiogram within 7 days

Exclusion Criteria

• Missing ECG signals
• Missing LVEF assessment in echocardiograms

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Tri-Service General Hospital

OTHER

Sponsor Role: lead

Responsible Party

Wei-Ting Liu

Clinical Doctor, Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Hualien Armed Forces General Hospital

Hualien City, , Taiwan

Kaohsiung Armed Forces General Hospital Gangshan Branch

Kaohsiung City, , Taiwan

Kaohsiung Armed Forces General Hospital

Kaohsiung City, , Taiwan

Zuoying Armed Forces General Hospital

Kaohsiung City, , Taiwan

Tri-Service General Hospital Keelung Branch

Keelung, , Taiwan

Tri-Service General Hospital Penghu Branch

Pengfu, , Taiwan

Kaohsiung Armed Forces General Hospital Pingtung Branch

Pingtung City, , Taiwan

Taichung Armed Forces General Hospital Zhongqing Branch

Taichung, , Taiwan

Taichung Armed Forces General Hospital

Taichung, , Taiwan

Tri-Service General Hospital Beitou Branch

Taipei, , Taiwan

Tri-Service General Hospital Songshan Branch

Taipei, , Taiwan

Taoyuan Armed Forces General Hospital Hsinchu Branch

Taoyuan District, , Taiwan

Taoyuan Armed Forces General Hospital

Taoyuan District, , Taiwan

Countries

Taiwan

Central Contacts

Wei-Ting Liu, M.D.

Role: CONTACT

wtliucv@gmail.com

+886287923311 ext. 15809

References

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.

Reference Type: BACKGROUND

PMID: 35330455 (View on PubMed)

Other Identifiers

B202405084

Identifier Type: -

Identifier Source: org_study_id