Peer-Reviewed Publication
ERJ Open Res2025;11(5)September 1, 2025Journal Article
Clinical validation of a machine-learned, point-of-care system to IDENTIFY pulmonary hypertension.
Dalton McLean1, John Rommel2, John A Steuter3, William S Carroll4, Mark Rabbat5, Sudarshan Rajagopal6, Venkatraman Srinivasan7, Dean J Kereiakes8, Michael C Roberts9, Abhijit Raval10, Navid Nemati11, Farhad Fathieh11, Timothy Burton11, Horace R Gillins12, Ian Shadforth12, Shyam Ramchandani11, Charles R Bridges12, Vallerie V McLaughlin13
1LeBauer Cardiovascular Research Foundation, Greensboro, NC, USA.
2Novant Health New Hanover Regional Medical Center, Wilmington, NC, USA.
3Bryan Heart, Lincoln, NE, USA.
4Cardiology Associates of North Mississippi, Tupelo, MS, USA.
5Loyola University Medical Center, Maywood, IL, USA.
6Duke University Medical Center, Durham, NC, USA.
7Allegheny Health Network Research Institute, Pittsburgh, PA, USA.
8The Lindner Research Center at The Christ Hospital, Cincinnati, OH, USA.
9Lexington Medical Center Heart & Vascular, West Columbia, SC, USA.
10AnMed Health, Anderson, SC, USA.
11Analytics For Life, Inc, Toronto, ON, Canada.
12CorVista Health, Inc., Bethesda, MD, USA.
13University of Michigan Health, Ann Arbor, MI, USA.
Abstract
BACKGROUND: Pulmonary hypertension (PH) is a collection of diverse disorders, defined by mean pulmonary artery pressure (mPAP) ≥21 mmHg (most recent guidelines) or ≥25 mmHg (previous guidelines, that underpins the field's past work) measured by right heart catheterisation (RHC). Considering the difficulties in diagnosing PH and the subsequent treatment delays, there is a need for novel diagnostics…
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