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Can we improve prediction of dystonia outcomes following deep brain stimulation? A study of clinical, genetic, radiologic and stimulation-related variables

Mulroy, Eoin; (2024) Can we improve prediction of dystonia outcomes following deep brain stimulation? A study of clinical, genetic, radiologic and stimulation-related variables. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Deep brain stimulation (DBS) has, in the last 2 decades, become a widely available and mainstream treatment option for patients with medically refractory dystonia. Though frequently life-changing in terms of its therapeutic effects, variability in outcomes mean that an uncomfortable degree of uncertainty frequently accompanies the procedure. Indeed, between 20-25% of patients undergoing DBS for dystonia will fail to obtain significant benefit, despite optimal pre-operative selection. The reasons underlying this outcome variability remain poorly understood, but the end result is that up to a quarter of patients offered the procedure may be unnecessarily exposed to surgical risks without the chance of obtaining meaningful benefit. Research into the factors underlying outcome variability have been conducted in the past, but are often hampered by low case numbers, variability in dystonia classification systems, and non-uniformity in surgical and device programming approaches. Further, recent advances in our understanding of the genetic underpinnings of dystonia have been slow to filter through to clinical practice, meaning that the influence of genotype on outcomes has not been thoroughly assessed in previously published cohorts. The aim of this research study is to examine, in a large single-centre cohort, the clinical, genetic, stimulation-related and imaging variables which may help to predict outcome and thus optimise patient selection for the procedure. This study also, for the first time, reports on the utility of novel deep brain local field potential sensing technologies in a dystonia cohort. The results highlight a number of clinical, genetic and pre-operative structural imaging findings which appear to influence outcome following DBS. Further, the stimulation sweet-spots (the optimal location within a stimulation target to deliver stimulation), and phenotype-specific network effects of stimulation in these regions are defined. Finally, we report our findings from local-field potential recordings in a small sub-cohort, and the difficulties encountered. The main outcome of this study is to highlight the variety of factors which potentially influence outcomes following DBS for dystonia. This, combined with the heterogeneity of individual dystonic disorders, means that accurate outcome prediction on an individual level is likely to remain extremely difficult. We conclude by suggesting novel areas for future research, reviewing treatment options for non-responders and place this study and its findings in a wider health economic context.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Can we improve prediction of dystonia outcomes following deep brain stimulation? A study of clinical, genetic, radiologic and stimulation-related variables
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. - Some third party copyright material has been removed from this e-thesis.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
URI: https://discovery.ucl.ac.uk/id/eprint/10192244
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