A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension

A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled 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.

Pulmonary hypertension is often underdiagnosed due to extensive category of etiology. The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality.

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