A canonical correlation analysis of the relationship between clinical attributes and patient-specific hemodynamic indices in adult pulmonary hypertension. (March 2020) - Piskin, Senol; Patnaik, Sourav S.; Han, David; Bordones, Alifer D.; Murali, Srinivas; Finol, Ender A.
Highlights: Modeling of pulmonary hypertension hemodynamics using computational fluid dynamics. Reconstruction of 34 patient-specific pulmonary artery models up to the seventh-generation bifurcation. Surface and volume-based indices used to characterize pulmonary hemodynamics. Evaluation of the relationship between computational hemodynamic metrics and clinical measurements. Abstract: 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 ≤  R 2 ≤ 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.