OEM News

CorVista Health, Mayo Clinic Advance Next-Gen Pulmonary Hypertension Diagnostics

Prospective study to evaluate how the CorVista-PH test may improve detection and risk stratification for suspected pulmonary hypertension.

By: Michael Barbella

Managing Editor

CorVista Health Inc. has announced a research collaboration with Mayo Clinic to evaluate the way next-generation, non-invasive diagnostics may transform the identification and management of pulmonary hypertension (PH), a complex and frequently underdiagnosed cardiovascular condition that affects an estimated 1% of the global population.

Pulmonary hypertension is a progressive and potentially life-threatening condition characterized by elevated pressure in the pulmonary arteries and increasing heart strain. Since symptoms such as shortness of breath, fatigue, and chest discomfort overlap with many other cardiopulmonary diseases, patients often experience prolonged and complex diagnostic pathways before receiving a definitive diagnosis. Earlier, more accurate identification remains a pressing need in pulmonary vascular medicine.

The U.S. Food and Drug Administration (FDA)-cleared CorVista-PH test, part of the CorVista System, is a new breed of cardiovascular diagnostics designed to analyze subtle patterns in cardiac and hemodynamic signals using machine-learned algorithms. The CorVista-PH test received breakthrough designation from the FDA and was validated through clinical trials involving more than 11,000 patients. The collaboration will pair this technology with Mayo Clinic’s expertise in cardiovascular research and pulmonary vascular disease to determine whether the CorVista-PH test can meaningfully improve the way physicians identify and evaluate pulmonary hypertension earlier in the patient journey.

“Collaborating with Mayo Clinic represents a defining moment for CorVista and for the advancement of pulmonary hypertension diagnostics,” CorVista Health President/CEO Adrian Lam said. “By combining Mayo’s research capabilities with CorVista’s next-generation diagnostic platform, we seek to fundamentally improve how pulmonary hypertension is identified in our efforts to move the field closer to earlier detection, more accurate referral, and better outcomes for patients.”

Under the collaboration, researchers are slated to conduct a prospective observational study to evaluate whether the CorVista-PH test can improve the detection and referral of patients with suspected pulmonary hypertension when used alongside current standard diagnostic approaches. By combining insights from referral patterns, biomarker correlations and invasive diagnostic confirmation, the study aims to build comprehensive evidence for earlier, more accurate detection and diagnosis at the point of care.

More broadly, the research reflects a growing recognition that advanced signal-based diagnostics and artificial intelligence may play a critical role in addressing persistent gaps in cardiovascular disease detection; particularly for conditions like pulmonary hypertension, where delayed diagnosis remains a major barrier to effective treatment.

The CorVista System is an FDA-cleared, non-invasive cardiovascular diagnostics platform designed to analyze cardiac and hemodynamic signals using machine-learned algorithms. The system synchronously collects physiological signals during a brief point-of-care test and applies advanced analytics to identify patterns associated with pulmonary hypertension (PH) and coronary artery disease (CAD). The CorVista System delivers actionable diagnostic insights without the use of radiation, contrast agents, injections, fasting, or exercise. The CorVista System is developed and manufactured by Analytics For Life Inc. and licensed to CorVista Health Inc.

CorVista Health aims to transform cardiovascular care through diagnostics that shorten the path from symptoms to diagnosis. By enabling earlier detection of complex cardiovascular conditions, CorVista aims to empower clinicians with actionable insights and improve patient outcomes across diverse care settings.

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