Computational four-chamber model of the heart for diastolic and systolic heart failure
Computational modeling integrating multi-scale knowledge of cardiac growth and remodeling
Existing single- or bi-ventricular models
Prediction of chronic alterations in wall thickness, chamber size, cardiac geometry, and secondary effects (papillary muscle dislocation, annular dilation, regurgitant flow, outflow obstruction)
A novel four-chamber computational model successfully simulates patient-specific cardiac growth and remodeling in heart failure, predicting both primary geometric changes and secondary structural effects.
Chronic heart failure is a medical condition that involves structural and functional changes of the heart and a progressive reduction in cardiac output. Heart failure is classified into two categories: diastolic heart failure, a thickening of the ventricular wall associated with impaired filling; and systolic heart failure, a dilation of the ventricles associated with reduced pump function. In theory, the pathophysiology of heart failure is well understood. In practice, however, heart failure is highly sensitive to cardiac microstructure, geometry, and loading. This makes it virtually impossible to predict the time line of heart failure for a diseased individual. Here we show that computational modeling allows us to integrate knowledge from different scales to create an individualized model for cardiac growth and remodeling during chronic heart failure. Our model naturally connects molecular events of parallel and serial sarcomere deposition with cellular phenomena of myofibrillogenesis and sarcomerogenesis to whole organ function. Our simulations predict chronic alterations in wall thickness, chamber size, and cardiac geometry, which agree favorably with the clinical observations in patients with diastolic and systolic heart failure. In contrast to existing single- or bi-ventricular models, our new four-chamber model can also predict characteristic secondary effects including papillary muscle dislocation, annular dilation, regurgitant flow, and outflow obstruction. Our prototype study suggests that computational modeling provides a patient-specific window into the progression of heart failure with a view towards personalized treatment planning.
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Martin Genet
Lik Chuan Lee
Brian Baillargeon
Annals of Biomedical Engineering
Stanford University
University of California, San Francisco
Michigan State University
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Genet et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69d56de575589c71d767d1a0 — DOI: https://doi.org/10.1007/s10439-015-1351-2