Prediction of Ventricular Mechanics After Pulmonary Valve Replacement in Tetralogy of Fallot by Biomechanical Modeling: A Step Towards Precision Healthcare
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
Inria-UTSW Associated Team TOFMOD
Inria
NV19-08-00071
Ministerstvo Zdravotnictví Ceské Republiky
Pogue Family Distinguished Chair
Pogue Family
PubMed
34853921
DOI
10.1007/s10439-021-02895-9
PII: 10.1007/s10439-021-02895-9
Knihovny.cz E-zdroje
- Klíčová slova
- Biomechanical modeling, Myocardial contractility, Valve replacement, Valvular heart disease, Ventricular overload,
- MeSH
- biomechanika MeSH
- chirurgická náhrada chlopně MeSH
- Fallotova tetralogie chirurgie MeSH
- individualizovaná medicína * MeSH
- lidé MeSH
- modely kardiovaskulární MeSH
- obstrukce výtoku ze srdeční komory patofyziologie chirurgie MeSH
- plicní chlopeň chirurgie MeSH
- pooperační komplikace patofyziologie chirurgie MeSH
- prediktivní hodnota testů MeSH
- remodelace komor MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Clinical indicators of heart function are often limited in their ability to accurately evaluate the current mechanical state of the myocardium. Biomechanical modeling has been shown to be a promising tool in addition to clinical indicators. By providing a patient-specific measure of myocardial active stress (contractility), biomechanical modeling can enhance the precision of the description of patient's pathophysiology at any given point in time. In this work we aim to explore the ability of biomechanical modeling to predict the response of ventricular mechanics to the progressively decreasing afterload in repaired tetralogy of Fallot (rTOF) patients undergoing pulmonary valve replacement (PVR) for significant residual right ventricular outflow tract obstruction (RVOTO). We used 19 patient-specific models of patients with rTOF prior to pulmonary valve replacement (PVR), denoted as PSMpre, and patient-specific models of the same patients created post-PVR (PSMpost)-both created in our previous published work. Using the PSMpre and assuming cessation of the pulmonary regurgitation and a progressive decrease of RVOT resistance, we built relationships between the contractility and RVOT resistance post-PVR. The predictive value of such in silico obtained relationships were tested against the PSMpost, i.e. the models created from the actual post-PVR datasets. Our results show a linear 1-dimensional relationship between the in silico predicted contractility post-PVR and the RVOT resistance. The predicted contractility was close to the contractility in the PSMpost model with a mean (± SD) difference of 6.5 (± 3.0)%. The relationships between the contractility predicted by in silico PVR vs. RVOT resistance have a potential to inform clinicians about hypothetical mechanical response of the ventricle based on the degree of pre-operative RVOTO.
Zobrazit více v PubMed
Arts, T., T. Delhaas, P. Bovendeerd, X. Verbeek, and F. W. Prinzen. Adaptation to mechanical load determines shape and properties of heart and circulation: the CircAdapt model. Am. J. Physiol.-Heart Circ. Physiol. 288:H1943–H1954, 2005. DOI
Asner, L., M. Hadjicharalambous, R. Chabiniok, D. Peresutti, E. Sammut, J. Wong, G. Carr-White, P. Chowienczyk, J. Lee, A. King, N. Smith, R. Razavi, and D. Nordsletten. Estimation of passive and active properties in the human heart using 3D tagged MRI. Biomech. Model. Mechanobiol. 15:1121–1139, 2016. DOI
Baumgartner, H., P. Bonhoeffer, N. M. S. De Groot, F. de Haan, J. E. Deanfield, N. Galie, M. A. Gatzoulis, C. Gohlke-Baerwolf, H. Kaemmerer, P. Kilner, F. Meijboom, B. J. M. Mulder, E. Oechslin, J. M. Oliver, A. Serraf, A. Szatmari, E. Thaulow, P. R. Vouhe, E. Walma, and Task Force on the Management of Grown-up Congenital Heart Disease of the European Society of Cardiology (ESC), Association for European Paediatric Cardiology (AEPC), and ESC Committee for Practice Guidelines (CPG). ESC Guidelines for the management of grown-up congenital heart disease (new version 2010). Eur. Heart J. 31:2915–2957, 2010. DOI
Bestel, J., F. Clément, and M. Sorine. A Biomechanical Model of Muscle Contraction. Berlin: Springer, 2001. https://doi.org/10.1007/3-540-45468-3_143 . DOI
Caruel, M., R. Chabiniok, P. Moireau, Y. Lecarpentier, and D. Chapelle. Dimensional reductions of a cardiac model for effective validation and calibration. Biomech. Model. Mechanobiol. 13:897–914, 2014. DOI
Castellanos, D. A., K. Škardová, A. Bhattaru, E. Berberoglu, G. Greil, A. Tandon, J. Dillenbeck, B. Burkhardt, T. Hussain, M. Genet, and R. Chabiniok. Left ventricular torsion obtained using equilibrated warping in patients with repaired tetralogy of Fallot. Pediatr. Cardiol. 42:1275–1283, 2021. DOI
Chabiniok, R., P. Moireau, P.-F. Lesault, A. Rahmouni, J.-F. Deux, and D. Chapelle. Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomech. Model. Mechanobiol. 11:609–630, 2012. DOI
Chabiniok, R., V. Y. Wang, M. Hadjicharalambous, L. Asner, J. Lee, M. Sermesant, E. Kuhl, A. A. Young, P. Moireau, M. P. Nash, D. Chapelle, and D. A. Nordsletten. Multiphysics and multiscale modelling, data–model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics. Interface Focus. 6:20150083, 2016. DOI
Chapelle, D., P. Le. Tallec, P. Moireau, and M. Sorine. An energy-preserving muscle tissue model: formulation and compatible discretizations. Int. J. Multiscale Comput. Eng. 10:189, 2012. https://doi.org/10.1007/978-3-030-21949-9_41 DOI
Donati, F., S. Myerson, M. M. Bissell, N. P. Smith, S. Neubauer, M. J. Monaghan, D. A. Nordsletten, and P. Lamata. Beyond Bernoulli: improving the accuracy and precision of noninvasive estimation of peak pressure drops. Circ. Cardiovasc. Imaging. 10:e005207, 2017. DOI
Fučík, R., R. Galabov, P. Pauš, P. Eichler, J. Klinkovský, R. Straka, J. Tintěra, and R. Chabiniok. Investigation of phase-contrast magnetic resonance imaging underestimation of turbulent flow through the aortic valve phantom: experimental and computational study using lattice Boltzmann method. Magn. Reson. Mater. Phys. Biol. Med. 33:649–662, 2020. DOI
Le Gall, A., F. Vallée, K. Pushparajah, T. Hussain, A. Mebazaa, D. Chapelle, É. Gayat, and R. Chabiniok. Monitoring of cardiovascular physiology augmented by a patient-specific biomechanical model during general anesthesia. A proof of concept study. PLoS ONE. 15:e0232830, 2020. DOI
Genet, M., L. C. Lee, B. Baillargeon, J. M. Guccione, and E. Kuhl. Modeling pathologies of diastolic and systolic heart failure. Ann. Biomed. Eng. 44:112–127, 2016. DOI
Geva, T. Repaired tetralogy of Fallot: the roles of cardiovascular magnetic resonance in evaluating pathophysiology and for pulmonary valve replacement decision support. J. Cardiovasc. Magn. Reson. 13:9, 2011. DOI
Göktepe, S., O. J. Abilez, and E. Kuhl. A generic approach towards finite growth with examples of athlete’s heart, cardiac dilation, and cardiac wall thickening. J. Mech. Phys. Solids. 58:1661–1680, 2010. DOI
Gusseva, M., T. Hussain, C. H. Friesen, P. Moireau, A. Tandon, C. Patte, M. Genet, K. Hasbani, G. Greil, D. Chapelle, and R. Chabiniok. Biomechanical modeling to inform pulmonary valve replacement in tetralogy of Fallot patients after complete repair. Can. J. Cardiol. 2021. https://doi.org/10.1016/j.cjca.2021.06.018 . PubMed DOI
Hadjicharalambous, M., R. Chabiniok, L. Asner, E. Sammut, J. Wong, G. Carr-White, J. Lee, R. Razavi, N. Smith, and D. Nordsletten. Analysis of passive cardiac constitutive laws for parameter estimation using 3D tagged MRI. Biomech. Model. Mechanobiol. 14:807–828, 2015. DOI
Hill, A. V. The heat of shortening and the dynamic constants of muscle. Proc. R. Soc. Lond. Ser. B. 126:136–195, 1938. DOI
Holzapfel, G. A., and R. W. Ogden. Constitutive modelling of passive myocardium: a structurally based framework for material characterization. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 367:3445–3475, 2009.
Hunter, P. J., and B. H. Smaill. The analysis of cardiac function: a continuum approach. Prog. Biophys. Mol. Biol. 52:101–164, 1988. DOI
Huxley, A. F. Muscle structure and theories of contraction. Prog. Biophys. Biophys. Chem. 7:255–318, 1957. DOI
Kerckhoffs, R. C. P., J. H. Omens, and A. D. McCulloch. A single strain-based growth law predicts concentric and eccentric cardiac growth during pressure and volume overload. Mech. Res. Commun. 42:40–50, 2012. DOI
Kimmig, F., D. Chapelle, and P. Moireau. Thermodynamic properties of muscle contraction models and associated discrete-time principles. Adv. Model. Simul. Eng. Sci. 6:6, 2019. DOI
Kroon, W., T. Delhaas, T. Arts, and P. Bovendeerd. Computational modeling of volumetric soft tissue growth: application to the cardiac left ventricle. Biomech. Model. Mechanobiol. 8:301–309, 2009. DOI
Lee, L. C., M. Genet, G. Acevedo-Bolton, K. Ordovas, J. M. Guccione, and E. Kuhl. A computational model that predicts reverse growth in response to mechanical unloading. Biomech. Model. Mechanobiol. 14:217–229, 2015. DOI
Lumens, J., and T. Delhaas. Cardiovascular modeling in pulmonary arterial hypertension: focus on mechanisms and treatment of right heart failure using the CircAdapt model. Am. J. Cardiol. 110:S39–S48, 2012. DOI
Lumens, J., T. Delhaas, B. Kirn, and T. Arts. Three-wall segment (TriSeg) model describing mechanics and hemodynamics of ventricular interaction. Ann. Biomed. Eng. 37:2234–2255, 2009. DOI
Lumens, J., S. Fan Chun-Po, J. Walmsley, D. Yim, C. Manlhiot, A. Dragulescu, L. Grosse-Wortmann, L. Mertens, W. Prinzen Frits, T. Delhaas, and K. Friedberg Mark. Relative impact of right ventricular electromechanical dyssynchrony versus pulmonary regurgitation on right ventricular dysfunction and exercise intolerance in patients after repair of tetralogy of Fallot. J. Am. Heart Assoc. 8:e010903, 2019. DOI
Lurz, P., J. Nordmeyer, V. Muthurangu, S. Khambadkone, G. Derrick, R. Yates, M. Sury, P. Bonhoeffer, and A. M. Taylor. Comparison of bare metal stenting and percutaneous pulmonary valve implantation for treatment of right ventricular outflow tract obstruction. Circulation. 119:2995–3001, 2009. DOI
McCulloch, A. D., B. H. Smaill, and P. J. Hunter. Left ventricular epicardial deformation in isolated arrested dog heart. Am. J. Physiol. Heart Circ. Physiol. 252:H233–H241, 1987. DOI
Niederer, S. A., G. Plank, P. Chinchapatnam, M. Ginks, P. Lamata, K. S. Rhode, C. A. Rinaldi, R. Razavi, and N. P. Smith. Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy. Cardiovasc. Res. 89:336–343, 2011. DOI
Oosterhof, T., F. J. Meijboom, H. W. Vliegen, M. G. Hazekamp, A. H. Zwinderman, B. J. Bouma, A. P. J. van Dijk, and B. J. M. Mulder. Long-term follow-up of homograft function after pulmonary valve replacement in patients with tetralogy of Fallot. Eur. Heart J. 27:1478–1484, 2006. DOI
Quail, M. A., A. Frigiola, A. Giardini, V. Muthurangu, M. Hughes, P. Lurz, S. Khambadkone, J. E. Deanfield, V. Tsang, and A. M. Taylor. Impact of pulmonary valve replacement in tetralogy of Fallot with pulmonary regurgitation: a comparison of intervention and nonintervention. Ann. Thorac. Surg. 94:1619–1626, 2012. DOI
Regazzoni, F., D. Chapelle, and P. Moireau. Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics—applications in cardiovascular modeling. Int. J. Numer. Methods Biomed. Eng. 37:e3471, 2021. DOI
Ruijsink, B., K. Zugaj, K. Pushparajah, and R. Chabiniok. Model-Based Indices of Early-Stage Cardiovascular Failure and Its Therapeutic Management in Fontan Patients. Cham: Springer, 2019. https://doi.org/10.1007/978-3-030-21949-9_41 . DOI
Ruijsink, B., K. Zugaj, J. Wong, K. Pushparajah, T. Hussain, P. Moireau, R. Razavi, D. Chapelle, and R. Chabiniok. Dobutamine stress testing in patients with Fontan circulation augmented by biomechanical modeling. PLOS ONE. 15:e0229015, 2020. DOI
Sainte-Marie, J., D. Chapelle, R. Cimrman, and M. Sorine. Modeling and estimation of the cardiac electromechanical activity. Comput. Struct. 84:1743–1759, 2006. DOI
Sermesant, M., R. Chabiniok, P. Chinchapatnam, T. Mansi, F. Billet, P. Moireau, J. M. Peyrat, K. Wong, J. Relan, K. Rhode, M. Ginks, P. Lambiase, H. Delingette, M. Sorine, C. A. Rinaldi, D. Chapelle, R. Razavi, and N. Ayache. Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation. Med. Image Anal. 16:201–215, 2012. DOI
Stergiopulos, N., B. E. Westerhof, and N. Westerhof. Total arterial inertance as the fourth element of the windkessel model. Am. J. Physiol. 276:H81-88, 1999. PubMed
Švihlová, H., J. Hron, J. Málek, K. R. Rajagopal, and K. Rajagopal. Determination of pressure data from velocity data with a view toward its application in cardiovascular mechanics. Part 1. Theoretical considerations. Int. J. Eng. Sci. 105:108–127, 2016. DOI
Valente, A. M., K. Gauvreau, G. E. Assenza, S. V. Babu-Narayan, J. Schreier, M. A. Gatzoulis, M. Groenink, R. Inuzuka, P. J. Kilner, Z. Koyak, M. J. Landzberg, B. Mulder, A. J. Powell, R. Wald, and T. Geva. Contemporary predictors of death and sustained ventricular tachycardia in patients with repaired tetralogy of Fallot enrolled in the INDICATOR cohort. Heart. 100:247–253, 2014. DOI
Wang, V. Y., P. M. F. Nielsen, and M. P. Nash. Image-Based Predictive Modeling of Heart Mechanics. Annu. Rev. Biomed. Eng. 17:351–383, 2015. DOI