Heart failure is a chronic disease associated with high hospitalization rates, costs and mortality. Dilated
cardiomyopathy is one of the common causes of non-ischemic heart failure and the leading cause of heart
transplantation in the young. The diversity of this disease, in its etiologies and clinical presentations, calls
for new strategies in risk stratification, medical management and therapy. Computational modeling of
cardiac function promises to improve our understanding of the disease pathomechanisms, identify new
prognostic cardiac biomarkers and simulate therapies. This project investigates the ability and precision
of the personalized multi-scale cardiac models in capturing systolic and diastolic function in patients
with dilated cardiomyopathy. Furthermore, the possibility of deriving new cardiac parameters from the
cardiac model is investigated.
After the generation of the computational models, the precision of the model in capturing the
systolic function was assessed. Upon completion, the ability of the model in capturing the diastolic
function was subsequently investigated. This project was performed in cooperation with Siemens AG. A
total of 58 patients with primary dilated cardiomyopathy were recruited in this project. Probable
secondary causes of dilated cardiomyopathy were excluded through comprehensive clinical phenotyping
including performing coronary angiography, echocardiography and cardiac MRI. Validated
mathematical models were integrated into the anatomical model to create a personalized multi-scale
mutli-physics cardiac model capturing patient specific cardiac anatomy, electrophysiology,
biomechanics and hemodynamics.
Parameters representing the systolic function, namely left ventricular ejection fraction and stroke
volume, from real measurements and from the simulated model were compared together. The mean
model error in the left ventricular ejection fraction was 3±1% (R=0.99, p
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