Department of Mathematics, North Carolina State University, Raleigh, NC, USA.
University of California, Irvine-Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, and Department of Biomedical Engineering, University of California, Irvine, CA, USA.
J R Soc Interface. 2023 Mar;20(200):20220735. doi: 10.1098/rsif.2022.0735. Epub 2023 Mar 1.
Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) greater than 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 cardiovascular systems-level computational models provide a potential non-invasive tool for determining additional indicators of disease severity. Using computational modelling, this study quantifies physiological parameters indicative of disease severity in nine PH patients. The model includes all four heart chambers, the pulmonary and systemic circulations. We consider two sets of calibration data: static (systolic and 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 multi-start inference and perform posterior uncertainty quantification. 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-modelling studies, supporting this type of analysis in translational PH research.
肺动脉高压（PH）定义为平均肺动脉压（mPAP）大于 20mmHg，其特征为肺血管阻力增加和肺动脉顺应性降低。PH 进展的可测量生物标志物很少，但疾病的确切诊断需要进行有创性右心导管检查（RHC）。患者特异性心血管系统水平的计算模型为确定疾病严重程度的其他指标提供了一种潜在的非侵入性工具。本研究使用计算模型量化了 9 名 PH 患者的疾病严重程度的生理参数。该模型包括四个心腔、肺循环和体循环。我们考虑了两组校准数据：静态（收缩和舒张值）RHC 数据以及静态和连续时间序列波形数据的组合。我们使用敏感性分析和多起始推理确定了模型校准的可识别参数子集，并进行了后验不确定性量化。结果表明，额外的波形数据能够在整个 PH 队列中准确校准右心房储器和泵功能。模型结果，包括冲程工作和肺阻力顺应性关系，反映了 PH 表型中的典型右心动力学。最后，我们表明估计的参数与之前的非建模研究一致，支持这种分析在转化 PH 研究中的应用。
