2026-10-13T17:49:01Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34032552 2026-09-30T13:12:48Z category_932 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Beyond detection: developing probabilistic hemodynamic phenotyping after a positive point-of-care pulmonary hypertension test.docx Farhad Fathieh (4382437) Navid Nemati (2500897) Timothy Burton (3401057) Horace R. Gillins (14123928) Ian Shadforth (5613803) Shyam Ramchandani (5613794) Charles R. Bridges (22778705) Health Informatics clinical decision support Gaussian mixture model hemodynamic phenotyping machine learning point-of-care diagnostics probabilistic classification pulmonary hypertension Background<p>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.</p>Methods<p>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 (n = 139). Hemodynamic subtypes were assigned per 2015 European Society of Cardiology/European Respiratory Society (ESC/ERS) criteria. A one-vs.-one (OvO) Gaussian mixture model (GMM) framework, combining unsupervised clustering with post hoc supervised assignment of subtype probabilities was applied to features derived from non-invasive orthogonal voltage gradient and photoplethysmographic signals to generate subject-level subtype probability estimates. Blinded validation in a large dataset has not yet been performed; development results are reported, supported by a small blinded independent dataset.</p>Results<p>At an exemplar post-hoc rule-out threshold of 16% pre-capillary probability, the GMM OvO approach yielded sensitivity 86% (95% CI: 71%–95%), specificity 51% (95% CI: 41%–61%), negative predictive value 88% (95% CI: 73%–96%) at an assumed pre-capillary prevalence of 34%, and a negative likelihood ratio (LR−) of 0.27 (95% CI: 0.07–0.70) on the development cohort. Supportive out-of-sample discrimination was observed in the blinded independent cohort.</p>Conclusions<p>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.</p> 2026-09-30T13:12:48Z Dataset Dataset 10.3389/fdgth.2026.1955810.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Beyond_detection_developing_probabilistic_hemodynamic_phenotyping_after_a_positive_point-of-care_pulmonary_hypertension_test_docx/34032552 CC BY 4.0