CorVista Health, Inc., a prominent digital health company specializing in cardiovascular disease diagnosis, has unveiled a groundbreaking machine-learned algorithm known as CorVista® Analysis. This algorithm aims to revolutionize the identification of patients with pulmonary hypertension (PH) and new onset symptoms. Recently presented at the American Thoracic Society (ATS) conference, the CorVista System’s proof of concept has garnered significant attention, following the FDA’s granting of Breakthrough Designation to aid in PH diagnosis. This article delves into the IDENTIFY PH study and highlights the potential of this innovative technology in the field of cardiovascular care.
Identifying PH Patients with New Onset Symptoms:
The IDENTIFY PH study, a prospective and multi-center investigation, was devised to train and validate machine-learned algorithms utilizing the CorVista System’s unique approach to measuring and analyzing electrical and hemodynamic signals at the point of care. The study encompassed patients exhibiting symptoms suggestive of PH, recruited from various healthcare centers across the United States. These patients underwent data acquisition, including transthoracic echocardiography (TTE) or right heart catheterization (RHC). The primary objective was to demonstrate the predictive capabilities of the machine learning algorithm for PH diagnosis, measured by the area under the curve of the receiver operating characteristic (AU-ROC).
Breakthrough Designation and Potential Benefits:
Don Crawford, President and CEO of CorVista Health, expressed satisfaction with the recent Breakthrough Designation for pulmonary hypertension. Moreover, he emphasized the significance of the proof-of-concept research presented during the IDENTIFY PH study, which showcases the potential to aid in the identification of PH patients with new onset symptoms. This breakthrough technology has the potential to revolutionize early detection of pulmonary hypertension within the clinical pathway.
CorVista System: Unveiling the Advanced Methodology:
The CorVista System employs a proprietary signal capture device to record synchronous orthogonal voltage gradients and photoplethysmographic waveforms from resting patients for 3.5 minutes. Alongside signal data, patient metadata such as birthdate, height, weight, and the results of TTE or RHC are collected. This multidisciplinary approach, integrated into a point-of-care test, exhibits promising results for early detection of PH in patients presenting with new onset symptoms of cardiovascular disease.
Advancing Cardiovascular Care with CorVista® Health:
Charles Bridges, M.D., Sc.D., Presenter and Chief Scientific Officer of CorVista Health, commended the multidisciplinary research for providing compelling evidence that a high-performance algorithm can be developed to assess the likelihood of PH in patients with new onset symptoms. By integrating this advancement into a point-of-care test, early detection of PH becomes a tangible reality within the clinical pathway. This development holds great promise for transforming cardiovascular care and improving patient outcomes.
Conclusion:
The CorVista® Analysis, a supervised machine-learned algorithm presented during the IDENTIFY PH study, represents a significant breakthrough in the field of pulmonary hypertension diagnosis. With the potential to aid in the identification of PH patients with new onset symptoms, this innovative technology can facilitate early detection and timely intervention. CorVista Health’s commitment to leveraging machine learning and real-world test data underscores their mission to transform cardiovascular care and enhance the patient experience. To learn more about CorVista Health and the CorVista System, please visit corvista.com.
Disclaimer: The CorVista System is currently an investigational device limited by Federal (or United States) law to investigational use.
