Inference in Cardiovascular Modelling Subject to Medical Interventions

Paun, L. M., Borowska, A. , Colebank, M. J., Olufsen, M. S. and Husmeier, D. (2021) Inference in Cardiovascular Modelling Subject to Medical Interventions. In: 3rd International Conference on Statistics: Theory and Applications (ICSTA'21), 29-31 Jul 2021, p. 109. ISBN 9781927877913 (doi: 10.11159/icsta21.109)

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Pulmonary hypertension (PH), i.e., high blood pressure in the lungs, is a serious medical condition that can damage the right ventricle of the heart and ultimately lead to heart failure. Standard diagnostic procedures are based on right-heart catheterization, which is an invasive technique that can potentially have serious side effects. Recent methodological advancements in fluid dynamics modelling of the pulmonary blood circulation system promise to mathematically predict the blood pressure based on non-invasive measurements of the blood flow. Thus, subsequent to PH diagnostication, further investigations would no longer require catheterization. However, in order for these alternative techniques to be applicable in the clinic, accurate model calibration and parameter estimation are paramount. Medical interventions taken to combat high blood pressure (as predicted from the mathematical model) alter the underlying cardiovascular physiology, thus interfering with the parameter estimation procedure. In the present study, we have carried out a series of cardiovascular simulations to assess the reliability of cardiovascular physiological parameter estimation in the presence of medical interventions. Our principal result is that if the closed-loop effect of medical interventions is accounted for, the model calibration provides accurate parameter estimates. This finding has important implications for the applicability of cardio-physiological modelling in the clinical practice.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Paun, Dr Mihaela and Husmeier, Professor Dirk and Borowska, Dr Agnieszka
Authors: Paun, L. M., Borowska, A., Colebank, M. J., Olufsen, M. S., and Husmeier, D.
Subjects:Q Science > QA Mathematics
R Medicine > R Medicine (General)
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
308255The SofTMech Statistical Emulation and Translation HubDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/T017899/1M&S - Statistics
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science