  Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure
(mPAP) $>$ 20 mmHg, is characterized by increased pulmonary vascular resistance
and decreased pulmonary arterial compliance. There are few measurable
biomarkers of PH progression, but a conclusive diagnosis of the disease
requires invasive right heart catheterization (RHC). Patient-specific
computational models of the cardiovascular system are a potential noninvasive
tool for determining additional indicators of disease severity. Using
computational modeling, this study quantifies physiological parameters
indicative of disease severity in nine PH patients. The model includes all four
heart chambers and the pulmonary and systemic circulations. We consider two
sets of calibration data: static (systolic \& diastolic values) RHC data and a
combination of static and continuous, time-series waveform data. We determine a
subset of identifiable parameters for model calibration using sensitivity
analyses and multistart inference, and carry out uncertainty quantification
post-inference. Results show that additional waveform data enables accurate
calibration of the right atrial reservoir and pump function across the PH
cohort. Model outcomes, including stroke work and pulmonary
resistance-compliance relations, reflect typical right heart dynamics in PH
phenotypes. Lastly, we show that estimated parameters agree with previous,
non-modeling studies, supporting this type of analysis in translational PH
research.

Quantitative Methods