Computational models of cardiac hypertrophy.

Area of Science:

• Cardiovascular Research
• Biomedical Engineering
• Computational Biology

Background:

• Cardiac hypertrophy involves heart mass increase due to hemodynamics, mechanical stimuli, and hormones.
• It occurs during development, exercise, pregnancy, and in cardiovascular diseases.
• Computational models show promise for predicting heart growth, especially with mechanical loading.

Purpose of the Study:

• Review the history and current state of cardiac growth models.
• Identify limitations of current models for clinical application.
• Propose a multiscale modeling approach for future cardiac growth prediction.

Main Methods:

• Literature review of cardiac growth models.
• Analysis of limitations in predicting growth regression, hemodynamics, and hormonal effects.
• Exploration of growth mechanics from other biomechanics fields.
• Proposal of a multiscale modeling approach integrating tissue and cell levels.

Main Results:

• Current cardiac growth models accurately predict growth under mechanical loading but have limitations.
• Key limitations include inability to predict growth regression, account for evolving hemodynamics, and incorporate hormonal/drug effects.
• Lessons from other biomechanics fields can inform cardiac modeling.

Conclusions:

• Existing computational models offer insights into cardiac hypertrophy but require refinement for clinical use.
• A multiscale approach combining tissue-level and cell-level signaling models is proposed to address current limitations.
• Future models aim to provide patient-specific predictions, incorporating hormonal influences, particularly for conditions like pregnancy-induced cardiac changes.