Does EDPVR Represent Myocardial Tissue Stiffness? Toward a Better Definition

TL;DR

This study questions whether the organ-level EDPVR metric accurately reflects tissue-level myocardial stiffness, highlighting the need for more precise, image-based in-silico methods for assessing myocardial tissue properties.

Contribution

The paper introduces a modeling approach that examines the relationship between EDPVR-derived stiffness and actual tissue-level myocardial stiffness using a two-parameter material model.

Findings

EDPVR metric shows low sensitivity to tissue parameters

Relationship between EDPVR stiffness and tissue stiffness varies with parameters

Highlights limitations of EDPVR as a tissue stiffness indicator

Abstract

Accurate assessment of myocardial tissue stiffness is pivotal for the diagnosis and prognosis of heart diseases. Left ventricular diastolic stiffness ($\beta$) obtained from the end-diastolic pressure-volume relationship (EDPVR) has conventionally been utilized as a representative metric of myocardial stiffness. The EDPVR can be employed to estimate the intrinsic stiffness of myocardial tissues through image-based in-silico inverse optimization. However, whether $\beta$, as an organ-level metric, accurately represents the tissue-level myocardial tissue stiffness in healthy and diseased myocardium remains elusive. We developed a modeling-based approach utilizing a two-parameter material model for the myocardium (denoted by $a_f$ and $b_f$) in image-based in-silico biventricular heart models to generate EDPVRs for different material parameters. Our results indicated a variable relationship between $\beta$ and the material parameters depending on the range of the parameters. Interestingly, $\beta$ showed a very low sensitivity to $a_f$, once averaged across several LV geometries, and even a negative correlation with $a_f$ for small values of $a_f$. These findings call for a critical assessment of the reliability and confoundedness of EDPVR-derived metrics to represent tissue-level myocardial stiffness. Our results also underscore the necessity to explore image-based in-silico frameworks, promising to provide a high-fidelity and potentially non-invasive assessment of myocardial stiffness.