This prospective observational cohort and validation study aims to create a patient dataset of point-of-care cardiovascular waveforms for validation of Imperial College London artificial intelligence (AI) algorithms trained for the detection of cardiovascular disease (CVD), using said waveforms as input, with a focus on pulmonary hypertension (PH).
The study will recruit 1,000 unselected patients attending Imperial College Healthcare NHS Trust for routine echocardiography. Each patient will undergo a non-invasive examination using a smart stethoscope that records 3- lead electrocardiogram (ECG) and phonocardiogram (PCG) waveforms, in addition to the standard echocardiography parameters. Baseline demographic data and medical history will also be collected, and a chart review will be performed at 24 months to capture any subsequent morbidity or mortality. The study will validate the AI algorithms by comparing their performance to echocardiography results, the current gold standard for CVD diagnosis. The primary outcome measures will include performance characteristics - sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F-1 Score of the AI algorithm in detecting CVD.
