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.
Age
50–85
Sex
ALL
Healthy volunteers
Not accepted
You may be eligible if
Men or women, ≥ 50 to 85 years of age
At least one 12-lead ECG within 3 months
You may not be if
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
Clareo Health | A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
