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Effect of Varying Pericardial Boundary Conditions on Whole Heart Function: A Computational Study

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Functional Imaging and Modeling of the Heart (FIMH 2023)

Abstract

Pericardiectomy is recommended therapy for pericarditis, an inflammation of the pericardial layers that surround the heart and play a central role in maintaining cardiac performance. In some cases, the pericardium can be repaired or patched. However, the impact of changes in the pericardium on cardiac function is not clear. The objective of this study is to analyze the effect of the pericardium on whole-heart function by varying normal Robin boundary conditions (BCs) applied on the ventricular epicardium. A piece-wise linear penalty function was defined using two parameters that were varied to describe the regional scaling of normal spring stiffness from apex to base. Gaussian process emulators were used to perform a global sensitivity analysis on how the varying pericardial BCs affect cardiac biomechanics in four-chamber heart models. Our results have shown that pressure- and volume-derived biomarkers change by less than 25% due to variations in the pericardium, with more variation in the right ventricle compared to the left ventricle. On the other hand, measurements for systolic motion exhibited a range of variability greater than 100% of the baseline mean. We predict that the pericardium has limited impact on measures of global function but impacts measures of local cardiac biomechanics.

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References

  1. Afanasyeva, M., Georgakopoulos, D., Fairweather, D., Caturegli, P., Kass, D., Rose, N.: Novel model of constrictive pericarditis associated with autoimmune heart disease in interferon-\(\gamma \)-knockout mice. Circulation 110(18), 2910–2917 (2004)

    Article  Google Scholar 

  2. Alter, P., Figiel, J., Rupp, R., Bachmann, G., Maisch, B., Rominger, M.: MR, CT, and PET imaging in pericardial disease. Heart Fail. Rev. 18(3), 289–306 (2013)

    Article  Google Scholar 

  3. Augustin, C., et al.: A computationally efficient physiologically comprehensive 3D–0D closed-loop model of the heart and circulation. Comput. Methods Appl. Mech. Eng. 386, 114092 (2021)

    Article  MATH  Google Scholar 

  4. Augustin, C., et al.: Anatomically accurate high resolution modeling of human whole heart electromechanics: a strongly scalable algebraic multigrid solver method for nonlinear deformation. J. Comput. Phys. 305, 622–646 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  5. Barrows, R., et al.: The effect of heart rate and atrial contraction on left ventricular function. Comput. Cardiol. 498, 1–4 (2022)

    Google Scholar 

  6. Bayer, J., et al.: Universal ventricular coordinates: a generic framework for describing position within the heart and transferring data. Med. Image Anal. 45, 83–93 (2018)

    Article  Google Scholar 

  7. Bayer, J., Blake, R., Plank, G., Trayanova, N.: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann. Biomed. Eng. 40(10), 2243–2254 (2012)

    Article  Google Scholar 

  8. Bitcon, C., Tousignant, C.: The effect of pericardial incision on right ventricular systolic function: a prospective observational study. Can. J. Anesth./J. Can. d’anesthésie 64(12), 1194–1201 (2017)

    Google Scholar 

  9. Bols, J., Degroote, J., Trachet, B., Verhegghe, B., Segers, P., Vierendeels, J.: A computational method to assess the in vivo stresses and unloaded configuration of patient-specific blood vessels. J. Comput. Appl. Math. 246, 10–17 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chang, S., Kim, H., Kim, Y., Cho, G., Oh, S., Sohn, D.: Role of pericardium in the maintenance of left ventricular twist. Heart 96(10), 785–790 (2010)

    Article  Google Scholar 

  11. Daughters, G., Frist, W., Alderman, E., Derby, G., Ingels, N., Jr., Miller, D.: Effects of the pericardium on left ventricular diastolic filling and systolic performance early after cardiac operations. J. Thorac. Cardiovasc. Surg. 104(4), 1084–1091 (1992)

    Article  Google Scholar 

  12. Gerach, T., et al.: Electro-mechanical whole-heart digital twins: a fully coupled multi-physics approach. Mathematics 9(11), 1247 (2021)

    Article  Google Scholar 

  13. Guccione, J., McCulloch, A., Waldman, L.: Passive material properties of intact ventricular myocardium determined from a cylindrical model (1991)

    Google Scholar 

  14. Herman, J., Usher, W.: SALib: an open-source python library for sensitivity analysis. J. Open Source Softw. 2(9), 97 (2017)

    Article  Google Scholar 

  15. Longobardi, S., et al.: Predicting left ventricular contractile function via gaussian process emulation in aortic-banded rats. Phil. Trans. R. Soc. A 378(2173), 20190334 (2020)

    Article  Google Scholar 

  16. Mangano, D.: The effect of the pericardium on ventricular systolic function in man. Circulation 61(2), 352–357 (1980)

    Article  Google Scholar 

  17. Marsh, K., et al.: Anti-inflammatory properties of amniotic membrane patch following pericardiectomy for constrictive pericarditis. J. Cardiothorac. Surg. 12(1), 1–4 (2017)

    Article  MathSciNet  Google Scholar 

  18. Melo, D., et al.: Impact of pericardiectomy on exercise capacity and sleep of patients with chronic constrictive pericarditis. PLoS ONE 14(10), e0223838 (2019)

    Article  Google Scholar 

  19. Neic, A., et al.: Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model. J. Comput. Phys. 346, 191–211 (2017)

    Article  MathSciNet  Google Scholar 

  20. Niederer, S., et al.: Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy. Cardiovasc. Res. 89(2), 336–343 (2011)

    Article  Google Scholar 

  21. Plank, G., et al.: The openCARP simulation environment for cardiac electrophysiology. Comput. Methods Programs Biomed. 208, 106223 (2021)

    Article  Google Scholar 

  22. Rodero, C., et al.: Calibration of cohorts of virtual patient heart models using Bayesian history matching. Ann. Biomed. Eng. 51, 1–12 (2022)

    Google Scholar 

  23. Rodero, C., et al.: Linking statistical shape models and simulated function in the healthy adult human heart. PLoS Comput. Biol. 17(4), e1008851 (2021)

    Article  Google Scholar 

  24. Roney, C., et al.: Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes. Med. Image Anal. 55, 65–75 (2019)

    Article  Google Scholar 

  25. Rösner, A., et al.: Changes in right ventricular shape and deformation following coronary artery bypass surgery-insights from echocardiography with strain rate and magnetic resonance imaging. Echocardiography 32(12), 1809–1820 (2015)

    Article  Google Scholar 

  26. Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., Tarantola, S.: Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index. Comput. Phys. Commun. 181(2), 259–270 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  27. Santiago, A., et al.: Fully coupled fluid-electro-mechanical model of the human heart for supercomputers. Int. J. Numer. Methods Biomed. Eng. 34(12), e3140 (2018)

    Article  MathSciNet  Google Scholar 

  28. Sobol, I.: Global sensitivity indices for nonlinear mathematical models and their monte Carlo estimates. Math. Comput. Simul. 55(1–3), 271–280 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  29. Simulating ventricular systolic motion in a four-chamber heart model with spatially varying robin boundary conditions to model the effect of the pericardium. J. Biomech. 101, 109645 (2020)

    Google Scholar 

  30. Vigmond, E., Hughes, M., Plank, G., Leon, L.: Computational tools for modeling electrical activity in cardiac tissue. J. Electrocardiol. 36, 69–74 (2003)

    Article  Google Scholar 

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Correspondence to Justina Ghebryal .

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Ghebryal, J. et al. (2023). Effect of Varying Pericardial Boundary Conditions on Whole Heart Function: A Computational Study. In: Bernard, O., Clarysse, P., Duchateau, N., Ohayon, J., Viallon, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science, vol 13958. Springer, Cham. https://doi.org/10.1007/978-3-031-35302-4_56

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  • DOI: https://doi.org/10.1007/978-3-031-35302-4_56

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