Non-invasive estimation of pressure drop across aortic coarctations: validation of 0D and 3D computational models with in vivo measurements

无创评估主动脉缩窄处的压力降:利用体内测量验证0D和3D计算模型

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Abstract

PURPOSE: Blood pressure gradient (ΔP) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of ΔP estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations. METHODS: Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17). 0D simulations were performed first and used to tune boundary conditions and initialize 3D simulations. ΔP across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation. RESULTS: The 0D simulations were extremely efficient (~15 secs computation time) compared to 3D simulations (~30 hrs computation time on a cluster). However, the 0D ΔP estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0D model classified patients with severe CoA requiring intervention (defined as ΔP ≥  20 mmHg) with 76% accuracy and 3D simulations improved this to 88%. CONCLUSION: Overall, a combined approach, using 0D models to efficiently tune and launch 3D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.

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