Computational Modeling for Cardiac Resynchronization Therapy

TY - JOUR

T1 - Computational Modeling for Cardiac Resynchronization Therapy

AU - Lee, Angela W C

AU - Mendonca Costa, Caroline

AU - Strocchi, Marina

AU - Rinaldi, Christopher A

AU - Niederer, Steven A

PY - 2018/4

Y1 - 2018/4

N2 - Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure (HF) patients with an electrical substrate pathology causing ventricular dyssynchrony. However 40–50% of patients do not respond to treatment. Cardiac modeling of the electrophysiology, electromechanics, and hemodynamics of the heart has been used to study mechanisms behind HF pathology and CRT response. Recently, multi-scale dyssynchronous HF models have been used to study optimal device settings and optimal lead locations, investigate the underlying cardiac pathophysiology, as well as investigate emerging technologies proposed to treat cardiac dyssynchrony. However the breadth of patient and experimental data required to create and parameterize these models and the computational resources required currently limits the use of these models to small patient numbers. In the future, once these technical challenges are overcome, biophysically based models of the heart have the potential to become a clinical tool to aid in the diagnosis and treatment of HF.

AB - Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure (HF) patients with an electrical substrate pathology causing ventricular dyssynchrony. However 40–50% of patients do not respond to treatment. Cardiac modeling of the electrophysiology, electromechanics, and hemodynamics of the heart has been used to study mechanisms behind HF pathology and CRT response. Recently, multi-scale dyssynchronous HF models have been used to study optimal device settings and optimal lead locations, investigate the underlying cardiac pathophysiology, as well as investigate emerging technologies proposed to treat cardiac dyssynchrony. However the breadth of patient and experimental data required to create and parameterize these models and the computational resources required currently limits the use of these models to small patient numbers. In the future, once these technical challenges are overcome, biophysically based models of the heart have the potential to become a clinical tool to aid in the diagnosis and treatment of HF.

U2 - 10.1007/s12265-017-9779-4

DO - 10.1007/s12265-017-9779-4

M3 - Review article

SN - 1937-5387

VL - 11

SP - 92

EP - 108

JO - Journal of cardiovascular translational research

JF - Journal of cardiovascular translational research

IS - 2

ER -