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Research Papers

Multiscale Computational Analysis of Right Ventricular Mechanoenergetics

[+] Author and Article Information
Ryan J. Pewowaruk

Mem. ASME
Biomedical Engineering,
University of Wisconsin—Madison,
2145 Engineering Centers Building,
1550 Engineering Drive,
Madison, WI 53706
e-mail: pewowaruk@wisc.edu

Jennifer L. Philip

Surgery,
University of Wisconsin—Madison,
2143 Engineering Centers Building,
1550 Engineering Drive,
Madison, WI 53706
e-mail: philip@surgery.wisc.edu

Shivendra G. Tewari

Molecular & Integrative Physiology,
University of Michigan—Ann Arbor,
2800 Plymouth Road,
North Campus Research Center,
Ann Arbor, MI 48109-5622
e-mail: tewarisg@gmail.com

Claire S. Chen

Mechanical Engineering,
University of Wisconsin—Madison,
2145 Engineering Centers Building,
1550 Engineering Drive,
Madison, WI 53706
e-mail: cchen394@wisc.edu

Mark S. Nyaeme

Biomedical Engineering,
University of Wisconsin—Madison,
2145 Engineering Centers Building,
1550 Engineering Drive,
Madison, WI 53706
e-mail: mnyaeme@wisc.edu

Zhijie Wang

Mechanical Engineering,
Colorado State University,
1301 Campus Delivery,
Fort Collins, CO 80521
e-mail: Zhijie.Wang@colostate.edu

Diana M. Tabima

Biomedical Engineering,
University of Wisconsin—Madison,
2144 Engineering Centers Building,
1550 Engineering Drive,
Madison, WI 53706
e-mail: dtabimamarti@wisc.edu

Anthony J. Baker

Medicine,
University of California—San Francisco,
4150 Clement St,
San Francisco, CA 94121;
VA Medical Center,
4150 Clement St.,
San Francisco, CA 94121
e-mail: Anthony.baker@ucsf.edu

Daniel A. Beard

Molecular & Integrative Physiology,
University of Michigan—Ann Arbor,
2800 Plymouth Road,
North Campus Research Center,
Ann Arbor, MI 48109-5622
e-mail: beardda@med.umich.edu

Naomi C. Chesler

Fellow ASME
Biomedical Engineering,
University of Wisconsin—Madison Medicine,
2146 Engineering Centers Building,
1550 Engineering Drive,
Madison, WI 53706
e-mail: naomi.chesler@wisc.edu

1Corresponding author.

Manuscript received December 1, 2017; final manuscript received April 13, 2018; published online May 24, 2018. Assoc. Editor: Rouzbeh Amini.

J Biomech Eng 140(8), 081001 (May 24, 2018) (15 pages) Paper No: BIO-17-1562; doi: 10.1115/1.4040044 History: Received December 01, 2017; Revised April 13, 2018

Right ventricular (RV) failure, which occurs in the setting of pressure overload, is characterized by abnormalities in mechanical and energetic function. The effects of these cell- and tissue-level changes on organ-level RV function are unknown. The primary aim of this study was to investigate the effects of myofiber mechanics and mitochondrial energetics on organ-level RV function in the context of pressure overload using a multiscale model of the cardiovascular system. The model integrates the mitochondria-generated metabolite concentrations that drive intracellular actin-myosin cross-bridging and extracellular myocardial tissue mechanics in a biventricular heart model coupled with simple lumped parameter circulations. Three types of pressure overload were simulated and compared to experimental results. The computational model was able to capture a wide range of cardiovascular physiology and pathophysiology from mild RV dysfunction to RV failure. Our results confirm that, in response to pressure overload alone, the RV is able to maintain cardiac output (CO) and predict that alterations in either RV active myofiber mechanics or RV metabolite concentrations are necessary to decrease CO.

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Figures

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Fig. 1

Schematic of mechanoenergetic computational model of the cardiovascular system adapted from Tewari et al. [6]. (a) Overall scheme. At the cellular level, release of calcium from the sarcoplasmic reticulum and metabolites ATP, ADP, and inorganic phosphates (Pi) drives actin-myosin cross-bridge kinetics, which drives myofiber mechanics dependent on sarcomere length. At the tissue level, myofiber mechanics that incorporate cross-bridge cycling, intracellular titin, and extracellular collagen and generate myofiber stress dependent on myofiber strain. At the organ level, ventricle mechanics and intraventricular interactions drive ventricular pressures dependent on ventricular volumes; the circulation provides ventricular afterload and the hemodynamic connection between the chambers. (b) Diagram of actin-myosin cross-bridging kinetics. Kinetic rates are functions of Ca2+ and metabolite concentrations. (c) Diagram of myofiber mechanics; Fpas: passive force, including contributions of titin and collagen, Fact: active force of actin-myosin cross-bridging, μ: viscous force, and FSE: corrective factor for sarcomere length. (d) Diagram of heart and circulation; C: compliance, R: Resistance, PA: pulmonary artery, PV: pulmonary veins, SV: systemic veins, Ao: aorta, sys: systemic vasculature, pul: pulmonary vasculature, RA: right atria, RV: right ventricle, LA: left atria, and LV: left ventricle.

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Fig. 2

Good agreement is observed between experimental [15] and simulated myofilament force versus pCa2+, especially at maximum myofilament force

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Fig. 7

EDPVR increases with RV dilation and increased RV stiffness decreases CO but not EDPVR. (a) Pressure overload increases RV end diastolic volume, (b) which results in increased EDPVR, (c) increasing RV stiffness decreases CO, (d) but instead of increasing EDPVR, increased RV stiffness decreases EDPVR. (e) With increased afterload, RV sarcomeres are elongated, and increased stiffness prevents this elongation. (f) With increased afterload, RV passive stress increases, and increasing RV stiffness further increases RV passive stress.

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Fig. 6

Parameter study correlating RV EF with myofilament force and metabolite concentration. (a) At 1x PVR, maximum myofilament force has a much stronger correlation with EF than (b) metabolite concentration has with EF. (c) At 2× PVR, maximum myofilament force still exhibits a stronger correlation with EF than (d) the correlation between metabolite concentration and EF. E. At 4× PVR, maximum myofilament force exhibits a similar correlation with EF as (f) the correlation between metabolite concentration and EF.

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Fig. 5

Comparison of MI experimental [30,33] and simulated PH-LHD. Error bars represent standard error. (a) LVSP is maintained in both experiments [30] and simulation. (b) LV EF is predicted to decrease slightly with LHD (HF metabolites in the LV). (c) LV EDP increase is not as severe in simulation as experiment [33]. (d) Simulations predict increased RV afterload, (e) while changing RV metabolites or myofilament force is necessary to decrease Ees, thus (f) VVC is predicted to decrease with LHD, and further decrease with impaired RV cellular function. (g) LHD alone overestimates RVSP, while changing myofilament force and metabolites slightly underestimates RVSP [30]. (h) CO is maintained both experimentally [30] and in simulation. (i) Simulations predict decreased RV EF.

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Fig. 4

Comparison of HySu experimental [16] and simulated PH. Error bars represent standard error. PH alone and PH + increased RV stiffness match all hemodynamic parameters well, but changing metabolite concentration and maximum force causes more severe decreases in RV function than observed experimentally.

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Fig. 3

Comparison of Bleo experimental [31] and simulated PH. Error bars represent interquartile range. ((a)–(c)) Simulation predicts preserved Ees until metabolites or maximum force changes, and that maximum force and metabolite changes result in further VVC decreases compared to PH alone. ((d)–(f)) PH alone and PH + increased RV stiffness overestimate RVSP and CO but match EF, while changing maximum force and metabolites matches CO but underestimates RVSP and EF.

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Fig. 8

Plot of time varying atrial compliance. When compliance is low, the atrium is contracting, and when compliance is high, the atrium is relaxed.

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