Beyond detection: developing probabilistic hemodynamic phenotyping after a positive point-of-care pulmonary hypertension test.

Background: Pulmonary hypertension (PH) is a heterogeneous condition in which hemodynamic subtype directs management and specialist referral. PH can be identified non-invasively at the point of care by a previously developed, regulatory-cleared machine-learned diagnostic (area under the receiver operating characteristic curve 0.95, sensitivity 82%, specificity 92%), but a positive test result raises a second clinical question: which patients have pre-capillary disease and therefore warrant referral to a specialist PH center. A probabilistic post-test phenotyping model was developed to estimate hemodynamic subtype probabilities and support rule-out of pre-capillary PH among test-positive patients. Methods: Subjects with right heart catheterization (RHC)-confirmed PH [mean pulmonary artery pressure (mPAP) ≥25 mmHg] enrolled in a prospective multisite trial (NCT04031989) who tested positive on the previously validated point-of-care PH diagnostic formed the analysis cohort ( Results: At an exemplar Conclusions: A GMM OvO probabilistic model applied to non-invasive point-of-care signals can generate PH subtype probability estimates and may support rule-out of pre-capillary disease among true-positive patients. The model reuses signals already acquired for PH detection and can therefore be implemented as a software addition to an existing point-of-care workflow. Independent blinded validation is required to comprehensively characterize model performance.