Dataset Information

A Multiphysics Biventricular Cardiac Model: Simulations With a Left-Ventricular Assist Device.

ABSTRACT: Computational models have become essential in predicting medical device efficacy prior to clinical studies. To investigate the performance of a left-ventricular assist device (LVAD), a fully-coupled cardiac fluid-electromechanics finite element model was developed, incorporating electrical activation, passive and active myocardial mechanics, as well as blood hemodynamics solved simultaneously in an idealized biventricular geometry. Electrical activation was initiated using a simplified Purkinje network with one-way coupling to the surrounding myocardium. Phenomenological action potential and excitation-contraction equations were adapted to trigger myocardial contraction. Action potential propagation was formulated within a material frame to emulate gap junction-controlled propagation, such that the activation sequence was independent of myocardial deformation. Passive cardiac mechanics were governed by a transverse isotropic hyperelastic constitutive formulation. Blood velocity and pressure were determined by the incompressible Navier-Stokes formulations with a closed-loop Windkessel circuit governing the circulatory load. To investigate heart-LVAD interaction, we reduced the left ventricular (LV) contraction stress to mimic a failing heart, and inserted a LVAD cannula at the LV apex with continuous flow governing the outflow rate. A proportional controller was implemented to determine the pump motor voltage whilst maintaining pump motor speed. Following LVAD insertion, the model revealed a change in the LV pressure-volume loop shape from rectangular to triangular. At higher pump speeds, aortic ejection ceased and the LV decompressed to smaller end diastolic volumes. After multiple cycles, the LV cavity gradually collapsed along with a drop in pump motor current. The model was therefore able to predict ventricular collapse, indicating its utility for future development of control algorithms and pre-clinical testing of LVADs to avoid LV collapse in recipients.

SUBMITTER: Ahmad Bakir A

PROVIDER: S-EPMC6142745 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Multiphysics Biventricular Cardiac Model: Simulations With a Left-Ventricular Assist Device.

Ahmad Bakir Azam A Al Abed Amr A Stevens Michael C MC Lovell Nigel H NH Dokos Socrates S

Frontiers in physiology 20180911

Computational models have become essential in predicting medical device efficacy prior to clinical studies. To investigate the performance of a left-ventricular assist device (LVAD), a fully-coupled cardiac fluid-electromechanics finite element model was developed, incorporating electrical activation, passive and active myocardial mechanics, as well as blood hemodynamics solved simultaneously in an idealized biventricular geometry. Electrical activation was initiated using a simplified Purkinje ...[more]

PMID: 30271353

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Project description:This study investigates the use of a Bayesian statistical model to address the limited predictive capacity of existing risk scores derived from multivariate analyses. This is based on the hypothesis that it is necessary to consider the interrelationships and conditional probabilities among independent variables to achieve sufficient statistical accuracy.Right ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation.Data used for this study were derived from 10,909 adult patients from the Inter-Agency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who had a primary LVAD implanted between December 2006 and March 2014. An initial set of 176 pre-implantation variables were considered. RVF post-implant was categorized as acute (<48 h), early (48 h to 14 daysays), and late (>14 days) in onset. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables employing an open source Bayesian inference engine.The acute RVF model consisted of 33 variables including systolic pulmonary artery pressure (PAP), white blood cell count, left ventricular ejection fraction, cardiac index, sodium levels, and lymphocyte percentage. The early RVF model consisted of 34 variables, including systolic PAP, pre-albumin, lactate dehydrogenase level, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation, and body mass index. The late RVF model included 33 variables and was predicted mostly by peripheral vascular resistance, model for end-stage liver disease score, albumin level, lymphocyte percentage, and mean and diastolic PAP. The accuracy of all Bayesian models was between 91% and 97%, with an area under the receiver operator characteristics curve between 0.83 and 0.90, sensitivity of 90%, and specificity between 98% and 99%, significantly outperforming previously published risk scores.A Bayesian prognostic model of RVF based on the large, multicenter INTERMACS registry provided highly accurate predictions of acute, early, and late RVF on the basis of pre-operative variables. These models may facilitate clinical decision making while screening candidates for LVAD therapy.

Dataset Information

A Multiphysics Biventricular Cardiac Model: Simulations With a Left-Ventricular Assist Device.

Publications

A Multiphysics Biventricular Cardiac Model: Simulations With a Left-Ventricular Assist Device.

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