Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements

Priya J Nair; Martin R Pfaller; Seraina A Dual; Doff B McElhinney; Daniel B Ennis; Alison L Marsden

doi:10.1007/s10439-024-03457-5

Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements

Ann Biomed Eng. 2024 May;52(5):1335-1346. doi: 10.1007/s10439-024-03457-5. Epub 2024 Feb 10.

Authors

Priya J Nair^{1

2}, Martin R Pfaller^{2

3

4

5}, Seraina A Dual⁶, Doff B McElhinney^{2

3

4

7}, Daniel B Ennis^{2

4

8

9}, Alison L Marsden^{10

11

12

13

14}

Affiliations

¹ Department of Bioengineering, Stanford University, Stanford, CA, USA.
² Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
³ Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA.
⁴ Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA.
⁵ Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
⁶ Department of Biomedical Signaling and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
⁷ Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA.
⁸ Department of Radiology, Stanford University, Stanford, CA, USA.
⁹ Division of Radiology, VA Palo Alto Healthcare System, Palo Alto, CA, USA.
¹⁰ Department of Bioengineering, Stanford University, Stanford, CA, USA. amarsden@stanford.edu.
¹¹ Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA. amarsden@stanford.edu.
¹² Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA. amarsden@stanford.edu.
¹³ Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA. amarsden@stanford.edu.
¹⁴ Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA. amarsden@stanford.edu.

PMID: 38341399
DOI: 10.1007/s10439-024-03457-5

Abstract

Blood pressure gradient ( $Δ P$ ) 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 $Δ P$ estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations. 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. $Δ P$ across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation. The 0D simulations were extremely efficient ( $\sim$ 15 s computation time) compared to 3D simulations ( $\sim$ 30 h computation time on a cluster). However, the 0D $Δ P$ 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 $Δ P$ $\geq$ 20 mmHg) with 76% accuracy and 3D simulations improved this to 88%. 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.

Keywords: Aortic coarctation; Computational fluid dynamics; Hemodynamics.

MeSH terms

Aortic Coarctation* / diagnostic imaging
Blood Flow Velocity
Blood Pressure
Computer Simulation
Humans
Magnetic Resonance Angiography / methods

Abstract

MeSH terms

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