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.
Men or women, ≥ 50 to 85 years of age
At least one 12-lead ECG within 3 months
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
Parallel
AI-ECG Guidance
Diagnostic testParticipants 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.
Pulmonary arterial pressure > 50 mmHg
The composite endpoint is defined as detecting pulmonary hypertension \> 50mmHg by echocardiography, indicating high risk for pulmonary hypertension.
Left atrial enlargement on a parasternal long axis view
The endpoint measures the size of left atrium \> 40mm on a parasternal long axis view by echocardiography.
Left atrial enlargement by left atrium volume index
The endpoint measures the size of left atrium volume index \> 29 mL/m2 in sinus rhythm or \> 40 mL/m2 in AF by echocardiography.
Right ventricular enlargement on a parasternal long axis view
The endpoint measures the size of right ventricular basal dimension \> 27mm by echocardiography.
New onset of left ventricular dysfunction
The endpoint measures the number and proportion of LVEF \< 50%.
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