Data Sheet 1_Beyond detection: developing probabilistic hemodynamic phenotyping after a positive point-of-care pulmonary hypertension test.docx

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>