Personalized Computer Simulation of Diastolic Function in Heart Failure

Abstract

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.

Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges

“…The majority of previously published patient-specific cardiovascular models fall under the mission of the “Virtual Physiological Human” project which is to capture numerous and varied fragments of knowledge into predictive and personalized models that will make possible the investigation of the human body as a whole [ 77 ], although it should be appreciated that patient-specific cardiovascular models have long been used for surgical planning [ 23 , 24 ]. The parameters and variables for these patient-specific models rarely have a direct correlate with clinically relevant quantities, and while this fact complicates the validation process, it provides unique opportunities to improve our understanding of the underlying mechanisms of health and disease within a clinical context [ 29 , 31 , 78 ].…”

A Novel Nonlinear Parameter Estimation Method of Soft Tissues

“…A precise model of soft tissues is essential to achieve immersive virtual surgery. Nevertheless, it remains challenging to build a precise model of soft tissues because soft tissues tend to possess rather complicated elastic behavior [6] , [7] .…”

Computational Cardiology — A New Discipline of Translational Research

“…In a Research Highlight, the potential of protein-coding sequences in the non-coding transcriptome is discussed [7] . Finally, more complex data integration approaches are described [8] , leading to the whole-heart simulation of diastolic dysfunction and heart failure [9] .…”