Patient-specific modelling of pulmonary arterial hypertension: wall shear stress correlates with disease severity

TL;DR

Researchers used patient-specific models to show that wall shear stress in blood vessels correlates with the severity of pulmonary arterial hypertension, suggesting a non-invasive way to diagnose and monitor the disease.

Contribution

This study introduces wall shear stress as a potential non-invasive biomarker for diagnosing and assessing pulmonary arterial hypertension severity.

Findings

Patient-specific CFD models matched in-vivo hemodynamics with a strong correlation (R2 = 0.84) between TAWSS and mean pulmonary pressure.

Low time-averaged wall shear stress correlates with high pulmonary pressure in PAH patients.

The study suggests TAWSS could serve as a computational biomarker for PAH diagnosis and stratification.

Abstract

Pulmonary arterial hypertension (PAH) requires an invasive right heart catheter (RHC) procedure for diagnosis. Patients can present with initial symptoms and interact with healthcare institutes for up to 3 years before referral for diagnosis. Thus, there is a great need to develop non-invasive tools, to better screen patients and improve early diagnosis rates.

seven patients diagnosed and treated for PAH were included in this study. Patient-specific computational fluid dynamics (CFD) models were built for all patients, with all model parameters tuned using non-invasive imaging data, including CT, cardiac MR, echocardiogram, and 4D-flow MRI scans–crucially, a 3D inlet velocity profile was derived from 4D-flow MRI.

CFD models were quantitatively and qualitatively well matched with in-vivo 4D-flow hemodynamics. A linear correlation of R2 = 0.84 was found between CFD derived time-averaged wall shear stress (TAWSS) and RHC measured mean pulmonary pressure (key diagnostic value): low TAWSS correlated with high pressure.

This study highlights TAWSS as a potential computational biomarker for PAH. The clinical use of TAWSS to diagnose and stratify PAH patients has the potential to greatly improve patient outcomes. Further work is ongoing to validate these findings in larger cohorts.

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