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Updated: Mar 21, 2026
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention
Yanhang Zhang1, Victor H Barocas2, Scott A Berceli3
1Departments of Mechanical Engineering and Biomedical Engineering, Boston University, Boston, MA, USA. yanhang@bu.edu.
Insights
Multi-scale and multi-physical models enhance understanding of cardiovascular diseases (CVDs). These models integrate biology, medicine, imaging, and biomechanics for better diagnostics and interventions.
Area of Science:
- Multiscale and multiphysical modeling in cardiovascular research.
- Integration of biology, medicine, imaging, and biomechanics for disease understanding.
Background:
- Cardiovascular diseases (CVDs) are a leading cause of death globally.
- Advancements in clinical diagnostics necessitate a deeper understanding of cardiovascular structure-function relationships.
- Computational models are crucial for bridging the gap between fundamental research and clinical applications.
Purpose of the Study:
- To review multi-scale and multi-physical models for understanding cardiovascular disease development and progression.
- To highlight the application of these models in designing clinical interventions and improving diagnostics.
- To discuss challenges and future directions for pre-clinical and clinical applications of cardiovascular modeling.
Main Methods:
- Review of multi-scale models in cardiac electrophysiology and mechanics.
- Introduction to computational models of vasculature mechanics and associated forces.
- Discussion of the interplay between diagnostic data and model refinement.
Main Results:
- Multi-scale cardiac models aid in diagnosis, clinical decision support, and personalized medicine for conditions like arrhythmia and heart failure.
- Vascular models elucidate disease progression and inform the design of interventions for various vascular conditions.
- The iterative process of model development and clinical data integration refines predictive capabilities.
Conclusions:
- Multi-scale and multi-physical modeling are essential tools for advancing cardiovascular research and clinical practice.
- These models offer significant potential for personalized medicine, improved diagnostics, and effective treatment strategies.
- Overcoming current barriers is key to unlocking enhanced insights and predictions in cardiovascular applications.
Abstract:
Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications.
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