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Identification of a hemodynamic parameter for assessing treatment outcome of EDAS in Moyamoya disease

Karunanithi, K., Han, C., Lee, C-JORCID: 0000-0002-9360-0923, Shi, W., Duan, L. and Qian, Y. (2015) Identification of a hemodynamic parameter for assessing treatment outcome of EDAS in Moyamoya disease. Journal of Biomechanics, 48 (2). pp. 304-309.

Link to Published Version: https://doi.org/10.1016/j.jbiomech.2014.11.029
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Abstract

This work is a novel attempt to incorporate computational fluid dynamics (CFD) techniques in the analysis of hemodynamic parameters of Moyamoya disease (MMD). Highly prevalent in Asian countries, MMD is characterised by progressive occlusion of the intracranial Internal Carotid Arteries (ICA). We intend to identify a reliable hemodynamic parameter that can be used to gauge treatment outcome. This will aid surgeons in the perioperative management of MMD patients. We carried out CFD analysis on eight patients (5 female, 3 male) with MMD treated by EDAS (encephalo-duro-arterio-synangiosis) between 2011 and 2012. All the eight patients presented with haemorrhage, with subsequent 4–12 month follow-up done using Magnetic Resonance Angiography (MRA) to capture auto-remodelling. We calculated percentage change in flow rate and pressure drop indicator (ΡDI) across the Left and Right ICA. Pressure drop indicator (PDI) is defined as the difference of pressure reduction within the carotid arteries, measured at post-op and follow up, using patient specific inflow rates. The measured percentage flow change and pressure reduction showed an increase at follow up for improved patients (characterised by angiography according to the method of Matsushima), who did not develop any complications after surgery. The inverse was observed in patients who were clinically classified as no change and retrogressed (according to the method of Matsushima) cases post-operation. This elucidates that our findings have instituted a new parameter that may well play a critical role as an assistive clinical decision making tool in MMD.

Item Type: Journal Article
Publisher: Elsevier
Copyright: © 2014 Elsevier Ltd
URI: http://researchrepository.murdoch.edu.au/id/eprint/53244
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