ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1585345
This article is part of the Research TopicDiagnostic and Predictive Roles of Computational Cardiovascular Hemodynamics in the Management of Cardiovascular DiseasesView all 15 articles
Patient-specific modelling of pulmonary arterial hypertension: wall shear stress correlates with disease severity
Provisionally accepted
- Imperial College London, London, United Kingdom
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Introduction: 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 three years before referral for diagnosis. Thus, there is a great need to develop noninvasive tools, to better screen patients and improve early diagnosis rates. Methods: 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 scanscrucially, a 3D inlet velocity profile was derived from 4D-flow MRI. Results: CFD models were quantitatively and qualitatively well matched with in-vivo 4D-flow hemodynamics. A linear correlation of R 2 = 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. Conclusions: 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.
Keywords: pulmonary arterial hypertension, computational fluid dynamics, 4D-flow MRI, Wall Shear Stress, Non-invasive assessment, computational biomarker
Received: 28 Feb 2025; Accepted: 03 Jun 2025.
Copyright: © 2025 Armour, Gopalan, Statton, O'Regan, Howard, Wilkins, Xu and Lawrie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chloe H Armour, Imperial College London, London, United Kingdom
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