The VALVE-AI Trial
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-ECG driven echocardiography.
- Who it may be relevant to
- Registry conditions: Valvular Heart Disease Patients. Basic parameters: 60 years — 85 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Taiwan
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
VALidation of Screening Valvular Heart Disease Using Electrocardiogram Powered by Artificial Intelligence: A Randomized Controlled Trial
Overview
The goal of this clinical trial is to learn if an artificial intelligence-powered electrocardiogram (AI-ECG) can help detect moderate or severe valvular heart diseases (VHD) in adults. The main question it aims to answer is: ．Can AI-ECG screening identify patients with significant heart valve diseases who may benefit from early echocardiography? Researchers will compare the rate of moderate or severe VHD detection between the experimental group and the control group to see if AI-ECG improve the detection rate of significant VHD. Participants will: * Be classified as high- or low-risk for VHD using an AI-ECG system * In the experimental group, high-risk participants will receive echocardiography based on AI-ECG results * In the control group, usual clinical care will be provided without routine echocardiography for AI-ECG high-risk results.
Detailed description
This randomized controlled trial investigates the effectiveness of an artificial intelligence-powered electrocardiogram (AI-ECG) system for early screening of moderate or severe valvular heart disease (VHD) in adults receiving routine ECG examinations. The study population consists of adult outpatients undergoing a standard 12-lead ECG for any clinical indication. Each ECG is analyzed by a validated deep learning algorithm that automatically classifies the patient's risk for significant VHD.
Participants identified as high-risk by the AI-ECG system are randomized into either an experimental group or a control group. In the experimental group, high-risk participants undergo transthoracic echocardiography to confirm or exclude moderate or severe VHD. In the control group, high-risk participants continue with usual clinical care without additional echocardiographic screening based solely on the AI-ECG result. Low-risk participants in both groups receive routine care without additional intervention.
The primary aim is to determine whether AI-guided ECG screening, coupled with targeted echocardiography in the experimental group, increases the detection rate of clinically significant VHD compared to usual care. Secondary objectives include evaluating the impact on timely diagnosis, downstream clinical management, and the feasibility of integrating AI-ECG screening into routine outpatient workflows.
The study will follow participants for up to 90 days post-randomization to assess the detection rate and related outcomes.
Interventions
- Diagnostic test AI-ECG driven echocardiography
The intervention utilizes a previously validated deep learning model based on 12-lead electrocardiogram (ECG) data to screen for moderate-to-severe valvular heart diseases (VHD). The model processes raw ECG signals and integrates age and sex to enhance prediction. (doi: 10.18632/aging.205835.) Participants identified as high-risk for any moderate-to-severe VHD by the algorithm of artificial intelligence-powered electrocardiogram (AI-ECG) in this intervention arm will receive transthoracic echoca
Primary outcome measures
- Composite of Any Moderate or Severe VHD by Echocardiography [Time frame: Within 90 days after randomization.]
Secondary outcome measures (5)
- Number of Participants with Moderate or Severe MR by Echocardiography [Time frame: Within 90 days after randomization.]
- Number of Participants with Moderate or Severe AR by Echocardiography [Time frame: Within 90 days after randomization.]
- Number of Participants with Moderate or Severe AS by Echocardiography [Time frame: Within 90 days after randomization.]
- Number of Participants with Moderate or Severe TR by Echocardiography [Time frame: Within 90 days after randomization.]
- Number of Participants with Other Cardiac Diseases by Echocardiography [Time frame: Within 90 days after randomization.]
Eligibility criteria
Inclusion criteria
- At least one 12-lead ECG within 1 year
- Age 60-85 years of age
Exclusion criteria
- Documented echocardiography within 3 years before indexed ECG
- Any known valvular heart disease
- History of any valvular surgery
- Post-heart transplant
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Screening
Study locations
Taiwan · 1 center
- Tri-Service General Hospital — Taipei
Publications
- Lin YT, Lin CS, Tsai CS, Tsai DJ, Lou YS, Fang WH, Lee YT, Lin C. Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases. Aging (Albany NY). 2024 May 16;16(10):8717-8731. doi: 10.18632/aging.205835. Epub 2024 May 16. PMID 38761181
Identifiers
NCT: NCT07023510 · VALVE-AI RCT