Abstract
Over the past several decades, increasingly sophisticated models of the heart have provided important insights into cardiac physiology and are increasingly used to predict the impact of diseases and therapies on the heart. In an era of personalized medicine, many envision patient-specific computational models as a powerful tool for personalizing therapy. Yet the complexity of current models poses important challenges, including identifying model parameters and completing calculations quickly enough for routine clinical use. We propose that early clinical successes are likely to arise from an alternative approach: relatively simple, fast, phenomenologic models with a small number of parameters that can be easily (and automatically) customized. We discuss examples of simple yet foundational models that have already made a tremendous impact on clinical education and practice, and make the case that reducing rather than increasing model complexity may be the key to realizing the promise of patient-specific modeling for clinical applications.
Original language | English |
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Pages (from-to) | 71-79 |
Number of pages | 9 |
Journal | Journal of Cardiovascular Translational Research |
Volume | 11 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Apr 2018 |
Keywords
- Cardiac mechanics
- Growth and remodeling
- Computational modeling
- Cardiology
- Biomechanics
- CANINE LEFT-VENTRICLE
- PRESSURE-VOLUME RELATIONSHIPS
- FINITE-ELEMENT MODEL
- MYOCARDIAL-INFARCTION
- REDUCTION SURGERY
- MECHANICAL LOAD
- PULSE PRESSURE
- GROWTH LAWS
- HEART
- ADAPTATION
- Prognosis
- Humans
- Ventricular Function
- Patient Selection
- Clinical Decision-Making
- Patient-Centered Care/methods
- Cardiovascular Diseases/diagnosis
- Models, Cardiovascular
- Patient-Specific Modeling
- Animals
- Hemodynamics
- LEFT-VENTRICLE
- SHAPE