• Main Authors: Ali Amr, Elham Kayvanpour, Farbod Sedaghat-Hamedani, Tiziano Passerini, Viorel Mihalef, Alan Lai, Dominik Neumann, Bogdan Georgescu, Sebastian Buss, Derliz Mereles, Edgar Zitron, Andreas E. Posch, Maximilian Würstle, Tommaso Mansi, Hugo A. Katus, Benjamin Meder
• Format: Article
• Language: English
• Published: Elsevier 2016-08-01
• Series: Genomics, Proteomics & Bioinformatics
• Subjects: Dilated cardiomyopathy; Tau; Myocardial stiffness; Computer-based 3D model; Personalized medicine; Diastolic function
• Online Access: http://www.sciencedirect.com/science/article/pii/S1672022916301061

The search for a parameter representing left ventricular relaxation from non-invasive and invasive diagnostic tools has been extensive, since heart failure (HF) with preserved ejection fraction (HF-pEF) is a global health problem. We explore here the feasibility using patient-specific cardiac computer modeling to capture diastolic parameters in patients suffering from different degrees of systolic HF. Fifty eight patients with idiopathic dilated cardiomyopathy have undergone thorough clinical evaluation, including cardiac magnetic resonance imaging (MRI), heart catheterization, echocardiography, and cardiac biomarker assessment. A previously-introduced framework for creating multi-scale patient-specific cardiac models has been applied on all these patients. Novel parameters, such as global stiffness factor and maximum left ventricular active stress, representing cardiac active and passive tissue properties have been computed for all patients. Invasive pressure measurements from heart catheterization were then used to evaluate ventricular relaxation using the time constant of isovolumic relaxation Tau (τ). Parameters from heart catheterization and the multi-scale model have been evaluated and compared to patient clinical presentation. The model parameter global stiffness factor, representing diastolic passive tissue properties, is correlated significantly across the patient population with τ. This study shows that multi-modal cardiac models can successfully capture diastolic (dys) function, a prerequisite for future clinical trials on HF-pEF.