Minimally-invasive estimation of patient-specific end-systolic elastance using a biomechanical heart model

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 E N (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 E N (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 E N (t). Then, we propose a minimally-invasive method based on a patient-specific bio-physical modelling to estimate the whole time-varying elastance curve E model (t). We compare E model es with the two single-beat estimation methods , and the normalised varying elastance curve E N,model (t) with E N (t) from published physiological experiments.
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Arthur Le Gall, Fabrice Vallée, Dominique Chapelle, Radomir Chabiniok. Minimally-invasive estimation of patient-specific end-systolic elastance using a biomechanical heart model. Proceedings of FIMH, Jun 2019, Bordeaux, France. ⟨hal-02153611⟩

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