Partial LVAD Restores Ventricular Outputs and Normalizes LV but not RV Stress Distributions in the Acutely Failing Heart in Silico

Sack, K; Baillargeon, Brian; Acevedo-Bolton, Gabriel; Genet, Martin; Rebelo, Nuno; Kuhl, Ellen; Klein, Liviu; Weiselthaler, Georg M.; Burkhoff, Daniel; Franz, Thomas; Guccione, Julius M.

doi:10.5301/ijao.5000520

Cited by 26 publications

(51 citation statements)

References 54 publications

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“…Following Göktepe and Kuhl, we assumed that the active stress distribution is entirely anisotropic. As in a previous work, we incorporated the time‐varying elastance model by Walker et al which accounts for the Frank‐Starling effect (ie, the intrinsic property of myocardium by which the strength of the heart's systolic contraction is directly proportional to its diastolic expansion) by an imposed active stress response's dependence on regional sarcomere lengths. This led to the following active stress in the cardiac myofiber direction:

σ_{af} ((),, t, {normalE}_{ff}) = \frac{{normalT}_{\max}}{2} \frac{{Ca}_{0}^{2}}{{Ca}_{0}^{2} + normalE {Ca}_{50}^{2} ((), {normalE}_{ff})} ((), 1 - cos ((), normalω ((),, t, {normalE}_{ff})))

where

normalE {Ca}_{50} ((), {normalE}_{ff}) = \frac{{Ca}_{0}_{\max}}{\sqrt{{normale}^{B (l (E_{ff}) - {normall}_{0})} - 1}}

normalω ((),, t, {normalE}_{ff}) = ({, \begin{matrix} normalπ \frac{t}{{normalt}_{0}} & when 0 \leq normalt \leq t_{0} \\ normalπ \frac{normalt - t_{0} + t_{r} ((), normall ((), {normalE}_{ff}))}{{normalt}_{normalr}} & when t_{0} \leq normalt \leq t_{0} + t_{r} ((), normalI ((), {normalE}_{ff})) \\ 0 & when normalt \geq t_{0} + t_{r} ((), normalI ((), {normalE}_{ff})) \end{matrix})

…”

Section: Methodsmentioning

confidence: 99%

“…Following Göktepe and Kuhl, 56 we assumed that the active stress distribution is entirely anisotropic. As in a previous work, 73 we incorporated the time-varying elastance model by Walker et al 16 which accounts for the Frank-Starling effect (ie, the intrinsic property of myocardium by which the strength of the heart's systolic contraction is directly proportional to its diastolic expansion 74 ) by an imposed active stress response's dependence on regional sarcomere lengths. This led to the following active stress in the cardiac myofiber direction:…”

Section: Active Tissue Behaviormentioning

confidence: 99%

“…The choice for the given time-varying elastance model ensured a smooth yet steep transition from zero to peak active tension at time t 0 followed by a smooth decline back to zero for the specified relaxation time t r . 73 Given that the tissue's active response is dependent on the cardiac cell's action potential (dependence on Ca 0 ), we assumed a homogeneous action potential pulse throughout the whole BV model which varies from −80 to 20 to −80 mV over 200 ms.…”

Section: Active Tissue Behaviormentioning

confidence: 99%

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Kinematic boundary conditions substantially impact in silico ventricular function

Peirlinck

Sack

Backer

et al. 2018

Numer Methods Biomed Eng

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Computational cardiac mechanical models, individualized to the patient, have the potential to elucidate the fundamentals of cardiac (patho-)physiology, enable non-invasive quantification of clinically significant metrics (eg, stiffness, active contraction, work), and anticipate the potential efficacy of therapeutic cardiovascular intervention. In a clinical setting, however, the available imaging resolution is often limited, which limits cardiac models to focus on the ventricles, without including the atria, valves, and proximal arteries and veins. In such models, the absence of surrounding structures needs to be accounted for by imposing realistic kinematic boundary conditions, which, for prognostic purposes, are preferably generic and thus non-image derived. Unfortunately, the literature on cardiac models shows no consistent approach to kinematically constrain the myocardium. The impact of different approaches (eg, fully constrained base, constrained epi-ring) on the predictive capacity of cardiac mechanical models has not been thoroughly studied. For that reason, this study first gives an overview of current approaches to kinematically constrain (bi) ventricular models. Next, we developed a patient-specific in silico biventricular model that compares well with literature and in vivo recorded strains. Alternative constraints were introduced to assess the influence of commonly used mechanical boundary conditions on both the predicted global functional behavior of the in-silico heart (cavity volumes, stroke volume, ejection fraction) and local strain distributions. Meaningful differences in global functioning were found between different kinematic anchoring strategies, which brought forward the importance of selecting appropriate boundary conditions for biventricular models that, in the near future, may inform clinical intervention. However, whilst statistically significant differences were also found in local strain distributions, these differences were minor and mostly confined to the region close to the applied boundary conditions.

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σ_{af} ((),, t, {normalE}_{ff}) = \frac{{normalT}_{\max}}{2} \frac{{Ca}_{0}^{2}}{{Ca}_{0}^{2} + normalE {Ca}_{50}^{2} ((), {normalE}_{ff})} ((), 1 - cos ((), normalω ((),, t, {normalE}_{ff})))

where

normalE {Ca}_{50} ((), {normalE}_{ff}) = \frac{{Ca}_{0}_{\max}}{\sqrt{{normale}^{B (l (E_{ff}) - {normall}_{0})} - 1}}

normalω ((),, t, {normalE}_{ff}) = ({, \begin{matrix} normalπ \frac{t}{{normalt}_{0}} & when 0 \leq normalt \leq t_{0} \\ normalπ \frac{normalt - t_{0} + t_{r} ((), normall ((), {normalE}_{ff}))}{{normalt}_{normalr}} & when t_{0} \leq normalt \leq t_{0} + t_{r} ((), normalI ((), {normalE}_{ff})) \\ 0 & when normalt \geq t_{0} + t_{r} ((), normalI ((), {normalE}_{ff})) \end{matrix})

…”

Section: Methodsmentioning

confidence: 99%

Section: Active Tissue Behaviormentioning

confidence: 99%

Section: Active Tissue Behaviormentioning

confidence: 99%

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Kinematic boundary conditions substantially impact in silico ventricular function

Peirlinck

Sack

Backer

et al. 2018

Numer Methods Biomed Eng

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show abstract

“…The LV and BV models are from the same animal. We used the Simulia Living Heart Project framework for the single LV model [14,15]. In the BV model, atria were excluded but their effects were considered via lumped parameter models [13].…”

Section: Computational Modelingmentioning

confidence: 99%

“…Moreover, we used strains provided by speckle tracking echocardiography of the heart. The ABAQUS living heart framework [14][15][16] was used to find computational strains using finite element (FE) modeling, whereas the whole optimization algorithm was conducted by ISIGHT. One of the possible applications of this project would be to quantify myocardial contractility after treatments with cardiac devices such as the left ventricle assistive device (LVAD) or MitraClip.…”

Section: Introductionmentioning

confidence: 99%

Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

Dabiri

Sack

Rebelo³

et al. 2019

Journal of Biomechanical Engineering

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We sought to calibrate mechanical properties of left ventricle (LV) based on three-dimensional (3D) speckle tracking echocardiographic imaging data recorded from 16 segments defined by American Heart Association (AHA). The in vivo data were used to create finite element (FE) LV and biventricular (BV) models. The orientation of the fibers in the LV model was rule based, but diffusion tensor magnetic resonance imaging (MRI) data were used for the fiber directions in the BV model. A nonlinear fiber-reinforced constitutive equation was used to describe the passive behavior of the myocardium, whereas the active tension was described by a model based on tissue contraction (Tmax). isight was used for optimization, which used abaqus as the forward solver (Simulia, Providence, RI). The calibration of passive properties based on the end diastolic pressure volume relation (EDPVR) curve resulted in relatively good agreement (mean error = −0.04 ml). The difference between the experimental and computational strains decreased after segmental strain metrics, rather than global metrics, were used for calibration: for the LV model, the mean difference reduced from 0.129 to 0.046 (circumferential) and from 0.076 to 0.059 (longitudinal); for the BV model, the mean difference nearly did not change in the circumferential direction (0.061) but reduced in the longitudinal direction from 0.076 to 0.055. The calibration of mechanical properties for myocardium can be improved using segmental strain metrics. The importance of realistic fiber orientation and geometry for modeling of the LV was shown.

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Precision medicine in human heart modeling

Peirlinck

Costabal

Jiang

et al. 2021

Biomech Model Mechanobiol

115

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Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

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Partial LVAD Restores Ventricular Outputs and Normalizes LV but not RV Stress Distributions in the Acutely Failing Heart in Silico

Cited by 26 publications

References 54 publications

Kinematic boundary conditions substantially impact in silico ventricular function

Kinematic boundary conditions substantially impact in silico ventricular function

Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

Precision medicine in human heart modeling

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