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Updated: Jan 15, 2026

Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Computational assessment of fibromuscular dysplasia-related renal artery stenosis
Luca Soliveri1, Maria Stoenoiu2, Michela Bozzetto1
1Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
Background:
Fibromuscular dysplasia (FMD) is a potentially curable cause of renovascular hypertension, primarily affecting middle-aged women. Percutaneous angioplasty is the recommended treatment for patients with hemodynamically significant renal artery stenosis (RAS). However, no established non-invasive method currently exists to estimate the pressure drop across stenosis and assess its hemodynamic impact. This study aimed to assess the clinical applicability of an image-based computational fluid dynamic (CFD) pipeline, functioning as a digital twin of the renal vasculature, for non-invasive assessment of fractional flow reserve (FFR) and trans-stenotic pressure drop in patients with FMD-related RAS.
Methods:
This retrospective study included 10 middle-aged women: six with FMD-related stenosis (two focal and four multifocal), two with atherosclerotic RAS and two healthy volunteers. For each subject, a patient-specific 3D surface model of the renal vasculature was reconstructed from medical images and CFD simulations were performed using Simvascular. FFR and trans-stenotic pressure drop were assessed across each renal artery.
Results:
The imaged-based CFD pipeline was feasible for all 10 subjects. A wide range of FFR (0.57 - >0.95) and pressure drops (11-73 mmHg) was observed across subjects, depending on the phenotype and severity of stenosis. The computed pressure drops and FFR values were consistent with the clinical decision on revascularization (AUC = 0.988), supporting the diagnostic relevance of the model.
Conclusion:
The proposed pipeline represents a promising non-invasive approach to assess the hemodynamic significance of RAS due to FMD. As a digital twin for personalized cardiovascular medicine, it could serve as valuable tool for selecting patients requiring angioplasty and predicting revascularization outcomes.
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