Key result
Adding dynamic waveforms to static measurements accurately calibrates RA function in pulmonary hypertension computational models.
Why the study?
There are few measurable biomarkers of pulmonary hypertension progression and diagnosis requires invasive right heart catheterization, motivating the use of patient-specific computational models to identify additional indicators of disease severity.
Patient-specific computational models incorporating continuous waveform data can accurately estimate physiological parameters of right heart dynamics in pulmonary hypertension.
Computational models may yield noninvasive PH biomarkers; hypothesis-generating and requires prospective RHC validation before any clinical role.
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.
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Colunga et al. (2021) studied Pulmonary hypertension (n=9). Dynamic time-series waveform data calibration vs. Static data calibration was evaluated on Model calibration accuracy for right atrial reservoir and pump function. Adding dynamic time-series waveform data to static measurements enabled accurate calibration of right atrial reservoir and pump function in a computational model of pulmonary hypertension.
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