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s1 {font: 8.0px Helvetica}Heart failure is a chronic disease associated with high hospitalization rates, costs and mortality. Dilatedcardiomyopathy is one of the common causes of non-ischemic heart failure and the leading cause of hearttransplantation in the young. The diversity of this disease, in its etiologies and clinical presentations, callsfor new strategies in risk stratification, medical management and therapy.

Computational modeling ofcardiac function promises to improve our understanding of the disease pathomechanisms, identify newprognostic cardiac biomarkers and simulate therapies. This project investigates the ability and precisionof the personalized multi-scale cardiac models in capturing systolic and diastolic function in patientswith dilated cardiomyopathy. Furthermore, the possibility of deriving new cardiac parameters from thecardiac model is investigated.

After the generation of the computational models, the precision of the model in capturing thesystolic function was assessed. Upon completion, the ability of the model in capturing the diastolicfunction was subsequently investigated. This project was performed in cooperation with Siemens AG. Atotal of 58 patients with primary dilated cardiomyopathy were recruited in this project.

Probablesecondary causes of dilated cardiomyopathy were excluded through comprehensive clinical phenotypingincluding performing coronary angiography, echocardiography and cardiac MRI. Validatedmathematical models were integrated into the anatomical model to create a personalized multi-scalemutli-physics cardiac model capturing patient specific cardiac anatomy, electrophysiology,biomechanics and hemodynamics.Parameters representing the systolic function, namely left ventricular ejection fraction and strokevolume, from real measurements and from the simulated model were compared together. The meanmodel error in the left ventricular ejection fraction was 3±1% (R=0.99, p<10-10).

At the same time, thevery high accuracy of the computed model was seen in the mean model error of only 9±6mL (R=0.96,p<10-10) with respect to stroke volume.Unlike systolic function, the assessment of diastolic function presents a challenge to treatingphysicians. Tau, the time constant of isovolumetric relaxation is a widely accepted surrogate for cardiacrelaxation and was chosen as a parameter representing diastolic function. The diastolic parameter fromthe personalized model, global stiffness factor, showed moderate correlations with Tau (?). Patients withelevated NT-proBNP had a higher correlation between HO and Tau. This accentuates the possible benefitof integrating cardiac molecular biomarkers with simulated parameters in search of new prognosticmarkers.40The potential use of computational modeling in patient risk stratification was evaluated throughthe correlation of the functional systolic parameter of the cardiac model, left ventricular active force,with a validated prognostic score, the Seattle Heart Failure Score.

The left ventricular active force,computed from the patient-specific cardiac model, significantly correlated with the Seattle Heart FailureScore (R=0.77, p=2.7×10-5). Although the presented correlations were not perfect and the results need tobe validated in larger patient cohorts, it presents the possibility of the identification of novel parameters,that cannot directly be derived from conventional clinical procedures.

This project could show that the creation of a multi-scale multi-physics cardiac model is feasiblein a clinical setting. The cardiac model was able to capture the systolic function of the heart accurately.Furthermore, the model was also able to capture the patient-specific diastolic function and proved to bepromising in identifying novel parameters of cardiac function.

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