Pillalamarri, Narasimha R.Pişkin, ŞenolPatnaik, Sourav S.Murali, SrinivasFinol, Ender A.2021-11-222021-11-222021Pillalamarri, N. R., Piskin, S., Patnaik, S. S., Murali, S., & Finol, E. A. (2021). Patient-Specific Computational Analysis of Hemodynamics in Adult Pulmonary Hypertension. Annals of biomedical engineering, 10.1007/s10439-021-02884-y. Advance online publication. https://doi.org/10.1007/s10439-021-02884-yhttps://doi.org/10.1007/s10439-021-02884-yhttps://hdl.handle.net/20.500.12713/2267Pulmonary hypertension (PH) is a progressive disease characterized by elevated pressure and vascular resistance in the pulmonary arteries. Nearly 250,000 hospitalizations occur annually in the US with PH as the primary or secondary condition. A definitive diagnosis of PH requires right heart catheterization (RHC) in addition to a chest computed tomography, a walking test, and others. While RHC is the gold standard for diagnosing PH, it is invasive and posseses inherent risks and contraindications. In this work, we characterized the patient-specific pulmonary hemodynamics in silico for diverse PH WHO groups. We grouped patients on the basis of mean pulmonary arterial pressure (mPAP) into three disease severity groups: at-risk ([Formula: see text], denoted with A), mild ([Formula: see text], denoted with M), and severe ([Formula: see text], denoted with S). The pulsatile flow hemodynamics was simulated by evaluating the three-dimensional Navier-Stokes system of equations using a flow solver developed by customizing OpenFOAM libraries (v5.0, The OpenFOAM Foundation). Quasi patient-specific boundary conditions were implemented using a Womersley inlet velocity profile and transient resistance outflow conditions. Hemodynamic indices such as spatially averaged wall shear stress ([Formula: see text]), wall shear stress gradient ([Formula: see text]), time-averaged wall shear stress ([Formula: see text]), oscillatory shear index ([Formula: see text]), and relative residence time ([Formula: see text]), were evaluated along with the clinical metrics pulmonary vascular resistance ([Formula: see text]), stroke volume ([Formula: see text]) and compliance ([Formula: see text]), to assess possible spatiotemporal correlations. We observed statistically significant decreases in [Formula: see text], [Formula: see text], and [Formula: see text], and increases in [Formula: see text] and [Formula: see text] with disease severity. [Formula: see text] was moderately correlated with [Formula: see text] and [Formula: see text] at the mid-notch stage of the cardiac cycle when these indices were computed using the global pulmonary arterial geometry. These results are promising in the context of a long-term goal of identifying computational biomarkers that can serve as surrogates for invasive diagnostic protocols of PH.eninfo:eu-repo/semantics/closedAccessComputational Fluid DynamicsImage-based ModelingPulmonary HypertensionPulsatile FlowRight Heart CatheterizationArticle34799807WOS:0007205834000022-s2.0-85119502153Q210.1007/s10439-021-02884-yN/A