Teo, Mario Kempes Chee Hseong ORCID: https://orcid.org/0000-0002-0051-3303 (2022) MOYAMOYA DISEASE IN THE WESTERN POPULATION AFTER EXTRACRANIAL-INTRACRANIAL BYPASS AND DEVELOPMENT OF PREDICTIVE MODELLING FOR PERIOPERATIVE STROKE RISK. PhD thesis, University of Sheffield.
Abstract
Moyamoya disease (MMD) is very rare in the western world, hence a lot is yet to be learnt including the long term clinical and functional outcome after bypass surgery. Stanford in California, USA has amongst the largest experience treating MMD patients with over 1500 extracranial-intracranial (ECIC) bypasses performed since 1990s. This thesis tests the hypothesis that:
1. Selective subgroups of patients with moyamoya disease in the western world are symptomatically and functionally cured after ECIC bypasses.
2. Robust and means tested scoring system could be designed to identify subgroups with good and poor outcome, in order to help with decision making process.
A systematic review and meta-analysis showing the superiority of direct and combined versus indirect bypass for MMD patients was presented, and knowledge gap identified.
In our surgical series, 96% and 73% of the adults and paediatric cohort respectively had direct revascularisation, with 7.3% per procedure 30-days stroke risk, and 0.6% per patient year long term stroke risk. Combining the outcome from questionnaires, clinical reviews, and radiological findings, 80-90% of MMD patients post revascularization have excellent long-term physical, social, functional wellbeing, with up to 26 years of follow-up.
Based on multivariate regression analyses, we developed the newly proposed Stanford Berlin MMD Grading system, taking into account the factors: A) age category, B) DSA score, C) modified MRI score, D) haemodynamic reserve score; and validated the grading system using another cohort of MMD patients treated at Stanford University from 2015-2018. We also used machine learning algorithm to develop a predictive modelling for post revascularisation stroke risk, with 93% accuracy. Further work is ongoing, combining the expertise from INSIGNEO Institute to improve the accuracy of the predictive modelling. With all this work and knowledge, it also resulted in significant improvement in UK moyamoya patient care.
Metadata
Supervisors: | Ross, Richard and Steinberg, Gary |
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Keywords: | Moyamoya, Extracranial to Intracranial Bypass, Western population, Meta-analysis, Machine learning, Perioperative stroke risks, Predictive Modelling, Stanford Berlin Moyamoya Grade |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Medicine (Sheffield) |
Academic unit: | Department of Neurosurgery, Stanford University Medical Centre, Stanford, USA |
Depositing User: | Dr Mario Kempes Chee Hseong Teo |
Date Deposited: | 16 Apr 2024 08:47 |
Last Modified: | 16 Apr 2024 08:47 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:34671 |
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