RECRUITINGINTERVENTIONAL
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial
About This Trial
This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP. We hypothesize that the AI-ECG model can early identify patients with pulmonary hypertension in high-risk patients, prompting further evaluation through echocardiography, potentially resulting in improving cardiovascular outcomes.
Who May Be Eligible (Plain English)
Who May Qualify:
- Men or women, ≥ 50 to 85 years of age
- At least one 12-lead ECG within 3 months
Who Should NOT Join This Trial:
- A diagnosis of PH WHO Groups 1, 2, 3, 4, or 5
- A diagnosis of hypertrophic cardiomyopathy, restrictive cardiomyopathy, constrictive pericarditis, cardiac amyloidosis, or infiltrative cardiomyopathy
- Prior heart, lung, or heart-lung transplants
- Any systolic pulmonary artery pressure \>50 mmHg by echocardiography before
- Echocardiography in 3 months before index ECG
Always talk to your doctor about whether this trial is right for you.
Original Eligibility Criteria
View original clinical language
Inclusion Criteria:
* Men or women, ≥ 50 to 85 years of age
* At least one 12-lead ECG within 3 months
Exclusion Criteria:
* A diagnosis of PH WHO Groups 1, 2, 3, 4, or 5
* A diagnosis of hypertrophic cardiomyopathy, restrictive cardiomyopathy, constrictive pericarditis, cardiac amyloidosis, or infiltrative cardiomyopathy
* Prior heart, lung, or heart-lung transplants
* Any systolic pulmonary artery pressure \>50 mmHg by echocardiography before
* Echocardiography in 3 months before index ECG
Treatments Being Tested
DIAGNOSTIC_TEST
AI-ECG Guidance
Participants undergo screening using the AI-ECG system. Those identified as high-risk for pulmonary hypertension receive echocardiography to confirm the diagnosis and guide subsequent management.
Locations (1)
National Defense Medical Center
Taipei, Taiwan