Predicting Hemodynamic Changes During Cardiac Hypertrophy

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Valaboju, Vignesh, Biomedical Engineering - School of Engineering and Applied Science, University of Virginia
Holmes, Jeffrey, EN-Biomed Engr Dept, University of Virginia

Heart failure (HF), a progressive disorder with high mortality rates, currently affects 6 million Americans. One of the main causes of HF is left ventricular (LV) hypertrophy: a structural abnormality that results from growth of LV walls during cardiomyopathy, arterial hypertension, and/or valve disease. Valve diseases such as aortic stenosis and mitral regurgitation cause pressure and volume overloading of the heart, respectively. Chronic progression of volume and pressure overload leads to large amounts of LV hypertrophy resulting in HF. Prospective patient-specific computational models of LV hypertrophy have the potential to aid diagnoses and drive development of tailored treatment plans to prevent progression of HF. As a result, our lab previously developed a rapid-computational growth model of the LV to model cardiac growth during pressure and volume overload. Although successful in accurately predicting growth, the model could only do so retrospectively because it relied on several hemodynamic parameters that were manually prescribed. To prospectively model patients, hemodynamic changes are often unknown and must be predicted to accurately model LV growth. Thus, this thesis aimed to develop a hemodynamic model that predicts hemodynamic changes to model LV growth during pressure and volume overload. (1) We modeled baroreceptor reflexes to predict short-term hemodynamic changes immediately following the onset of pressure or volume overload in canines. (2) We then modeled the renin-angiotensin II system to predict long-term hemodynamic changes several months after the onset of pressure and volume overload in canines. (3) Lastly, we coupled our short- and long-term hemodynamic models with the rapid-computational growth model to predict regression of LV hypertrophy following MitraClip implantation in individual mitral regurgitation patients. Overall, we built a hemodynamic and cardiac growth modeling framework to prospectively model patient-specific responses and help guide personalized treatments to prevent progression of HF.

MS (Master of Science)
Cardiovascular Engineering, LV Growth, Hemodynamics, Baroreceptors, MitraClip, Computational Modeling
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