On in-silico estimation of left ventricular end-diastolic pressure from cardiac strains

TL;DR

This study demonstrates that patient-specific computational cardiac models can non-invasively estimate left ventricular end-diastolic pressure and myocardial stiffness from cardiac strains, potentially improving diagnosis of diastolic dysfunction.

Contribution

The paper introduces a novel inverse in-silico modeling approach using high-fidelity patient-specific cardiac models to estimate LVEDP from imaging data.

Findings

Accurate estimation of LVEDP from cardiac strains

Feasibility of non-invasive LVEDP measurement

Potential clinical integration for early LVDD detection

Abstract

Left ventricular diastolic dysfunction (LVDD) is a group of diseases that adversely affect the passive phase of the cardiac cycle and can lead to heart failure. While left ventricular end-diastolic pressure (LVEDP) is a valuable prognostic measure in LVDD patients, traditional invasive methods of measuring LVEDP present risks and limitations, highlighting the need for alternative approaches. This paper investigates the possibility of measuring LVEDP non-invasively using inverse in-silico modeling. We propose the adoption of patient-specific cardiac modeling and simulation to estimate LVEDP and myocardial stiffness from cardiac strains. We have developed a high-fidelity patient-specific computational model of the left ventricle. Through an inverse modeling approach, myocardial stiffness and LVEDP were accurately estimated from cardiac strains that can be acquired from in vivo imaging, indicating the feasibility of computational modeling to augment current approaches in the measurement of ventricular pressure. Integration of such computational platforms into clinical practice holds promise for early detection and comprehensive assessment of LVDD with reduced risk for patients.