Collagen Turnover Modeling in a Rapid 0D Growth Framework

Abstract

Post-myocardial infarction (MI) growth and remodeling (G&R), commonly referred to as fibrosis, involves both geometric deformation and progressive stiffening of infarcted tissue due to collagen accumulation. While zero-dimensional (0D) cardiac G&R models have successfully reproduced organ-level adaptations post-infarction, they often neglect evolving tissue properties associated with collagen turnover. In this study, we address this limitation by incorporating a time-dependent stiffening law into a strain-driven 0D framework, extending the original model by Witzenburg et al. (2018). Collagen turnover (CT) was modeled using a phenomenological exponential function, calibrated against experimental hydroxyproline data. The model was validated against independent canine datasets and benchmarked against both the original reference and a baseline No CT simulation. While full time-course verification was not achieved - due to inconsistencies in baseline reported parameters - control and acute states were accurately reproduced. Critically, the CT-enhanced model reduced the mean standardized z-score (MSZ) by 57.8%, with the most substantial improvements seen in ventricular volume and diastolic pressure predictions. These results confirm the added value of explicitly modeling tissue-level remodeling and highlight the importance of accurate initialization to ensure long-term prediction fidelity in reduced-order frameworks.