Volume 18 Supplement 1

19th Annual SCMR Scientific Sessions

Open Access

Quantifying passive myocardial stiffness and wall stress in heart failure patients using personalized ventricular mechanics

  • Zhinuo Jenny Wang1,
  • Vicky Y Wang1,
  • Chris P Bradley1,
  • Martyn P Nash1, 2,
  • Alistair Young1, 4 and
  • Jie J Cao3
Journal of Cardiovascular Magnetic Resonance201618(Suppl 1):O17

https://doi.org/10.1186/1532-429X-18-S1-O17

Published: 27 January 2016

Background

Heart failure (HF) patients present with a spectrum of phenotypes, including preserved ejection fraction (HFpEF) and reduced EF (HFrEF). The underlying mechanisms of HFpEF and HFrEF may be related to mechanical factors such as stiffness and stress. This study aimed to quantify passive mechanical properties in HFrEF and HFpEF patients using personalised left ventricular (LV) biomechanical analysis.

Methods

Data from 9 HFrEF patients, 4 HFpEF patients, and 4 non-HF patients with normal LV function (control) were analyzed. LV finite element mechanical models were personalized using LV surface data segmented from cine cardiac magnetic resonance (CMR) images. All subjects underwent same day left and right cardiac catheterization. Beat-averaged intra-ventricular pressures were extracted from LV catheter traces and temporally aligned with the CMR images. A mechanical simulation of LV diastolic filling was optimized to each patient-specific geometry and pressure loading, incorporating non-linear myocardial anisotropic tissue behavior and myocyte fiber orientation derived from our published work. Global myocardial passive stiffness was estimated by optimization of the predicted LV surface displacements between diastasis and end-diastole to the observed CMR motion. Mid-ventricular end-diastolic fiber stress was also derived from the model.

Results

Volume and pressure changes between diastasis and end-diastole, and LV mass measurements are shown in Table 1 (mass and volume data have been normalised by body surface area). Myocardial stiffness was significantly higher for the HFrEF group (mean ± SEM 7.6 ± 1.6 kPa) compared to both the control group (1.3 ± 0.3 kPa, p < 0.01), and the HFpEF group (2.1 ± 0.4 kPa, p < 0.01), but no significant difference was found between control and HFpEF groups due to small sample sizes. Mid-ventricular end-diastolic fiber stress was also significantly larger in the HFrEF group (3.3 ± 0.4 kPa) compared to control (1.0 ± 0.2 kPa, p < 0.001) and HFpEF patients (1.3 ± 0.2 kPa, p < 0.05).
Table 1

Clinical characteristics of the HFrEF, HFpEF, and Control groups.

Group

DS pressure (mmHg)

ED pressure (mmHg)

DS volume index (mL/m2)

ED volume index (mL/m2)

Ejection fraction (%)

LV mass index (g/m2)

HFrEF

15 ± 3*

26 ± 3*

127 ± 11*‡

147 ± 10*‡

24 ± 3*‡

123 ± 7*

HFpEF

11 ± 1†

18 ± 1†

47 ± 7

59 ± 6

67 ± 2

95 ± 14

Control

6 ± 1

11 ± 1

48 ± 2

65 ± 6

59 ± 3

76 ± 5

*p < 0.05 HFrEF vs. Control; †p < 0.05 HFpEF vs. Control; ‡p < 0.05 HFpEF vs. HFrEF. Abbreviations: diastasis (DS), end-diastole (ED), left ventricle (LV). Volumes and masses indexed to body surface area.

Conclusions

Our findings suggest that personalized LV mechanical modeling may provide important diagnostic and therapeutic targets for HF management.

Authors’ Affiliations

(1)
Auckland Bioengineering Institute, University of Auckland
(2)
Department of Engineering Science, University of Auckland
(3)
The Heart Center, St Francis Hospital
(4)
Department of Anatomy with Radiology, University of Auckland

Copyright

© Wang et al. 2016

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement