Pulmonary hypertension (PH) is a progressive disease affecting approximately 10-52 cases per million, with a higher incidence in women, and with a high mortality associated with right ventricle (RV) failure. In this work, we explore the relationship between hemodynamic indices, calculated from in silico models of the pulmonary circulation, and clinical attributes of RV workload and pathological traits. Thirty-four patient-specific pulmonary arterial tree geometries were reconstructed from computed tomography angiography images and used for volume meshing for subsequent computational fluid dynamics (CFD) simulations. Data obtained from the CFD simulations were post-processed resulting in hemodynamic indices representative of the blood flow dynamics. A retrospective review of medical records was performed to collect the clinical variables measured or calculated from standard hospital examinations. Statistical analyses and canonical correlation analysis (CCA) were performed for the clinical variables and hemodynamic indices. Systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP), cardiac output (CO), and stroke volume (SV) were moderately correlated with spatially averaged wall shear stress (0.60 ≤ R2 ≤ 0.66; p < 0.05). Similarly, the CCA revealed a linear and strong relationship (ρ = 0.87; p << 0.001) between 5 clinical variables and 2 hemodynamic indices. To this end, in silico models of PH blood flow dynamics have a high potential for predicting the relevant clinical attributes of PH if analyzed in a group-wise manner using CCA.

中文翻译：

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成人肺动脉高压的临床特征与患者特异性血液动力学指标之间关系的典范相关性分析。

肺动脉高压（PH）是一种进行性疾病，每百万例患者感染约10-52例病例，女性发病率较高，并且与右心室（RV）衰竭相关的死亡率很高。在这项工作中，我们探索了由肺循环的计算机模型计算得出的血液动力学指标与RV工作量的临床属性和病理学特征之间的关系。从计算机断层扫描血管造影图像重建了34个患者特定的肺动脉树几何形状，并将其用于体积网格划分，以用于后续的计算流体动力学（CFD）模拟。从CFD模拟获得的数据经过后处理，得到代表血流动力学的血液动力学指数。对病历进行回顾性回顾，以收集从标准医院检查中测得或计算出的临床变量。对临床变量和血液动力学指标进行统计分析和规范相关分析（CCA）。收缩期肺动脉压（sPAP），舒张期肺动脉压（dPAP），心输出量（CO）和中风量（SV）与空间平均壁切应力呈中等相关性（0.60≤R2≤0.66; p <0.05）。同样，CCA揭示了5个临床变量和2个血液动力学指标之间的线性和强关系（ρ= 0.87； p << 0.001）。为此，如果使用CCA以分组方式进行分析，则PH血流动力学的计算机模型可以预测PH的相关临床属性。