Computational models of cardiac hypertrophy

Cardiac hypertrophy, defined as an increase in mass of the heart, is a complex process driven by simultaneous changes in hemodynamics, mechanical stimuli, and hormonal inputs. It occurs not only during preand post-natal development but also in adults in response to exercise, pregnancy, and a range of cardiovascular diseases. One of the most exciting recent developments in the field of cardiac biomechanics is the advent of computational models that are able to accurately predict patterns of heart growth in many of these settings, particularly in cases where changes in mechanical loading of the heart play an import role. These emerging models may soon be capable of making patient-specific growth predictions that can be used to guide clinical interventions. Here, we review the history and current state of cardiac growth models and highlight three main limitations of current approaches with regard to future clinical application: their inability to predict the regression of heart growth after removal of a mechanical overload, inability to account for evolving hemodynamics, and inability to incorporate known growth effects of drugs and hormones on heart growth. Next, we outline growth mechanics approaches used in other fields of biomechanics and highlight some potential lessons for cardiac growth modeling. Finally, we propose a multiscale modeling approach for future studies that blends tissue-level growth models with cell-level signaling models to incorporate the effects of hormones in the context of pregnancy-induced heart growth. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Cardiac hypertrophy, characterized by increased heart mass, is driven by changes in hemodynamics, mechanical stimuli, and hormonal inputs. Computational models have emerged as a key development in predicting heart growth patterns, with the potential for patient-specific predictions to guide clinical interventions. However, current approaches have limitations in predicting heart growth regression, evolving hemodynamics, and incorporating effects of drugs and hormones, highlighting the need for a multiscale modeling approach to address these challenges in cardiac growth modeling.