Minimally-Invasive Estimation of Patient-Specific End-Systolic Elastance Using a Biomechanical Heart Model

Arthur Le Gall, Fabrice Vallée, Dominique Chapelle, Radomír Chabiniok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The end-systolic elastance (Ees ) – the slope of the end-systolic pressure-volume relationship (ESPVR) at the end of ejection phase – has become a reliable indicator of myocardial functional state. The estimation of Ees by the original multiple-beat method is invasive, which limits its routine usage. By contrast, non-invasive single-beat estimation methods, based on the assumption of the linearity of ESPVR and the uniqueness of the normalised time-varying elastance curve EN(t) across subjects and physiology states, have been applied in a number of clinical studies. It is however known that these two assumptions have a limited validity, as ESPVR can be approximated by a linear function only locally, and EN(t) obtained from a multi-subject experiment includes a confidence interval around the mean function. Using datasets of 3 patients undergoing general anaesthesia (each containing aortic flow and pressure measurements at baseline and after introducing a vasopressor noradrenaline), we first study the sensitivity of two single-beat methods—by Sensaki et al. and by Chen et al.—to the uncertainty of EN(t). Then, we propose a minimally-invasive method based on a patient-specific biophysical modelling to estimate the whole time-varying elastance curve Emodel(t). We compare Eesmodel with the two single-beat estimation methods, and the normalised varying elastance curve EN, model(t) with EN(t) from published physiological experiments.

Original languageEnglish (US)
Title of host publicationFunctional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings
EditorsYves Coudière, Valéry Ozenne, Edward Vigmond, Nejib Zemzemi
PublisherSpringer Verlag
Pages266-275
Number of pages10
ISBN (Print)9783030219482
DOIs
StatePublished - 2019
Externally publishedYes
Event10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019 - Bordeaux, France
Duration: Jun 6 2019Jun 8 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11504 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019
Country/TerritoryFrance
CityBordeaux
Period6/6/196/8/19

Keywords

  • End-systolic elastance estimation
  • Patient-specific biophysical modelling
  • Time-varying elastance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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