An Unsupervised Approach to Derive Right Ventricular Pressure-Volume Loop Phenotypes in Pulmonary Hypertension.

Abstract

Although right ventricle (RV) dysfunction drives clinical worsening in pulmonary hypertension (PH), information about RV function has not been well integrated in PH risk assessment. The gold standard for assessing RV function and ventriculo-arterial coupling is the construction of multi-beat pressure-volume (PV) loops. PV loops are technically challenging to acquire and not feasible for routine clinical use. Therefore, we aimed to map standard clinically available measurements to emergent PV loop phenotypes. One hundred and one patients with suspected PH underwent right heart catheterization (RHC) with exercise, multi-beat PV loop measurement, and same-day cardiac magnetic resonance imaging (CMR). We applied unsupervised k-means clustering on 10 PV loop metrics to obtain three patient groups with unique RV functional phenotypes and times to clinical worsening. We integrated RHC and CMR measurements to train a random forest classifier that predicts the PV loop patient group with high discrimination (AUC = 0.93). The most informative variable for PV loop phenotype prediction was exercise mean pulmonary arterial pressure (mPAP). Distinct and clinically meaningful PV loop phenotypes exist that can be predicted using clinically accessible hemodynamic and RV-centric measurements. Exercise mPAP may inform RV pressure-volume relationships.