Computational fluid dynamics simulations showed high agreement with invasive measurements for identifying hemodynamically significant renal artery stenosis (sensitivity 70-100%, specificity 70-90%).
Observational (n=28)
Do computational fluid dynamics (CFD) simulations accurately estimate trans-stenotic pressure gradients compared to invasive measurements in patients with renal artery stenosis?
CFD simulations using CTA input demonstrate high accuracy for identifying hemodynamically significant renal artery stenosis compared to invasive pressure measurements.
Effect estimate: ICC 0.78 and 0.94 at baseline
Objective: In renal artery stenosis, treatment decisions based on pressure gradients can improve the assessment of lesions over anatomic grading. This could improve undertreatment and overtreatment rates. A fast and patient-friendly pressure assessment method is the use of computational fluid dynamics (CFD) simulations based on 3D vascular models. The objective of this study was to validate two computational fluid dynamics (CFD) models to estimate trans-stenotic pressure gradients, using invasive pressure measurements in patients with renal artery stenosis (RAS) as a reference. Design and method: We performed intra-arterial measurements at rest and during dopamine-induced hyperemia to assess the trans-stenotic pressure gradient in 28 patients with RAS. A pre-intervention CTA scan was used to simulate the pressure gradient with a CFD model using a strategy based on Murray's law (CFD-Mu) and cortical volume (CFD-C). The agreement between the simulated and measured pressure gradients was assessed using intraclass correlation coefficients (ICC), Bland-Altman analysis and diagnostic agreement on the presence of a hemodynamically significant stenosis, defined as a pressure gradient of above 10 mmHg at rest and above 20 mmHg during hyperemia. Results: In 20 patients, successful measurements and simulations were obtained. The ICC between measured pressure gradient and the CFD pressure gradient was 0.78 and 0.94 during baseline and 0.86 and 0.72 during hyperemia, for CFD-Mu and CFD-C, respectively. The sensitivity of CFD-Mu and CFD-C to identify a hemodynamically significant stenosis was 70% for both models at rest and 100% compared to the hyperemic measurements, whereas the specificity was 90% and 70% at rest and 79% and 72% during hyperemia, respectively.Conclusions: The results support the use of individualized CFD simulations for hemodynamic assessment of RAS using CTA as input. The CFD models demonstrated high accuracy for the identification of a hemodynamically significant stenosis.
Velde et al. (Fri,) conducted a observational in Renal artery stenosis (n=28). Computational fluid dynamics (CFD) simulations vs. Invasive pressure measurements was evaluated on Agreement between simulated and measured trans-stenotic pressure gradients (ICC 0.78 and 0.94 at baseline). Computational fluid dynamics simulations showed high agreement with invasive measurements for identifying hemodynamically significant renal artery stenosis (sensitivity 70-100%, specificity 70-90%).