The CorVista Analysis Provides a Supervised Machine-learned Algorithm to IDENTIFY PH Patients with New Onset Symptoms

CorVista Health, a leading digital health company dedicated to improving cardiovascular disease diagnosis, announces the presentation of a machine-learned algorithm to IDENTIFY PH at the American Thoracic Society (ATS) conference. This proof of concept showcases the effectiveness of a machine learned (ML) phase space electro-mechanical pulse wave analysis to identify pulmonary hypertension (PH) patients with new onset symptoms at the point-of-care. This presentation follows the recent achievement of the Breakthrough Designation from the FDA for the CorVista System to aid in the diagnosis of pulmonary hypertension.

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“This advancement, when integrated into a point-of-care test, holds great promise for early PH detection within the clinical pathway.”

This IDENTIFY PH study is a prospective, multi-center study designed to recruit patients for the training and validation of machine-learned algorithms based on the CorVista System’s novel approach to measure and analyze the patient’s electrical and hemodynamic signals at the point of care. The measurements are used to identify PH patients with new onset symptoms. The study enrolled consecutive patients across US-based healthcare centers who presented symptoms suggestive of PH. These patients underwent signal data acquisition and were enrolled with transthoracic echocardiography (TTE), or right heart catheterization (RHC) data. The primary objective of the study was to demonstrate a ML algorithm’s predictive capability for PH diagnosis, with diagnostic accuracy measured using the area under the curve of the receiver operating characteristic (AU-ROC).

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“After our recent Breakthrough Designation for pulmonary hypertension, we are now pleased to present this proof-of-concept research based on the IDENTIFY PH Study,” said Don Crawford, President and CEO of CorVista Health. “The data presented today demonstrates the potential of being able to aid identification of PH patients who present with new onset symptoms.”

Signals were collected using a proprietary signal capture device recording synchronous orthogonal voltage gradients and photoplethysmographic waveforms from resting patients for 3.5 minutes. Also collected were patient metadata (e.g., birthdate, height, weight) and the results of the patient TTE, or RHC.

“We are pleased to present this multidisciplinary work, which provides strong evidence that an algorithm with high performance can be developed to assess the likelihood of PH in patients with new onset symptoms of cardiovascular disease at the point-of-care,” said Charles Bridges, M.D., Sc.D., Presenter and Chief Scientific Officer of CorVista Health. “This advancement, when integrated into a point-of-care test, holds great promise for early PH detection within the clinical pathway.”

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