Personalized Computer Simulation of Diastolic Function in Heart Failure.

Area of Science:

• Cardiology
• Biomedical Engineering
• Computational Biology

Background:

• Heart failure with preserved ejection fraction (HF-pEF) is a significant global health challenge.
• Accurate assessment of left ventricular relaxation is crucial for diagnosing and managing HF-pEF.
• Current diagnostic tools have limitations in comprehensively evaluating diastolic parameters.

Purpose of the Study:

• To assess the feasibility of patient-specific cardiac computer modeling for capturing diastolic parameters in systolic heart failure (HF) patients.
• To identify novel computational parameters reflecting cardiac passive and active tissue properties.
• To correlate model-derived parameters with invasive measurements of ventricular relaxation.

Main Methods:

• Applied a multi-scale, patient-specific cardiac modeling framework to 58 patients with idiopathic dilated cardiomyopathy.
• Conducted comprehensive clinical evaluations including cardiac MRI, heart catheterization, and echocardiography.
• Computed novel parameters like global stiffness factor and maximum left ventricular active stress.
• Used invasive pressure measurements (Tau, τ) from heart catheterization to evaluate ventricular relaxation.

Main Results:

• The computed global stiffness factor, representing diastolic passive tissue properties, significantly correlated with the time constant of isovolumic relaxation (τ).
• Patient-specific models successfully captured diastolic (dys) function across varying degrees of systolic HF.
• Model parameters were evaluated against invasive measurements and clinical presentation.

Conclusions:

• Multi-modal cardiac models can effectively capture diastolic function in HF patients.
• The global stiffness factor shows promise as a non-invasive surrogate for assessing ventricular relaxation.
• This approach provides a foundation for future clinical trials targeting HF-pEF.