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

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Abstract

Blood pressure gradient (\(\Delta P\)) 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 \(\Delta P\) 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. \(\Delta P\) 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 \(\Delta P\) 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 \(\Delta P\) \(\ge\) 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.

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Data Availability

The datasets generated during this study are available in the Vascular Model Repository (vascularmodel.org).

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Acknowledgements

The authors thank the Stanford Research Computing Center for computational resources (Sherlock HPC cluster). This work is supported by the Vera Moulton Wall Center for Pulmonary Vascular Disease at Stanford University, NIH grant R01LM013120, and NSF grant 2105345. PJN is supported by the National Science Foundation Graduate Research Fellowship (DGE-1656518) and MRP is supported by the Stanford Maternal and Child Health Research Institute and the National Institutes of Health (K99HL161313).

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Authors and Affiliations

  1. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Priya J. Nair & Alison L. Marsden

  2. Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA

    Priya J. Nair, Martin R. Pfaller, Doff B. McElhinney, Daniel B. Ennis & Alison L. Marsden

  3. Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA

    Martin R. Pfaller, Doff B. McElhinney & Alison L. Marsden

  4. Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA

    Martin R. Pfaller, Doff B. McElhinney, Daniel B. Ennis & Alison L. Marsden

  5. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA

    Martin R. Pfaller & Alison L. Marsden

  6. Department of Biomedical Signaling and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden

    Seraina A. Dual

  7. Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA

    Doff B. McElhinney

  8. Department of Radiology, Stanford University, Stanford, CA, USA

    Daniel B. Ennis

  9. Division of Radiology, VA Palo Alto Healthcare System, Palo Alto, CA, USA

    Daniel B. Ennis

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  1. Priya J. Nair
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Contributions

PJN designed the study, performed the computational simulations and analysis, and drafted the manuscript; MRP implemented the 0D model formulation in SimVascular and supported the computational simulations and analysis; SAD extracted hemodynamic parameters from patient scans and supported the computational analysis; DBM provided patient data and clinical insights used to conceptualize this study; DBE provided overall supervision and advice to the research; ALM conceptualized the study and provided overall supervision to the research work. All authors have reviewed the final manuscript.

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Correspondence to Alison L. Marsden.

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Associate Editor Estefanía Peña oversaw the review of this article.

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Appendix

Appendix

The electric analog network and corresponding resistance, capacitance, and inductance values of each segment in the 0D network for one representative patient (P-1) are listed below (Table 4; Fig. 9).

Table 4 Resistance, capacitance, inductance, and stenosis coefficient for branches and segments in the 0D circuit network
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Fig. 9
figure 9

Electric analog network

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Nair, P.J., Pfaller, M.R., Dual, S.A. et al. Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements. Ann Biomed Eng 52, 1335–1346 (2024). https://doi.org/10.1007/s10439-024-03457-5

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