Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.726687
Title: Clinical heterogeneity, diagnostic features, outcomes of Guillain-Barré syndrome spectrum disorders : an analysis of IGOS UK data
Author: Chavada, Govind
ISNI:       0000 0004 6421 661X
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2017
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Abstract:
Introduction: GBS has a highly diverse clinical course and outcome. Currently available literature suggests that despite treatment about 20 % of patients remain disabled at one year and about 5 % patients die. These data come from clinical trials conducted between 1984 and 2006. Most of these studies included severe GBS cases. We conducted a multicentre prospective observational study looking at clinical and biological determinants of prognosis of GBS. As part of this study, I had an opportunity to analyse the data collected from 15 UK centres; looking at clinical and treatment patterns, various outcomes including ability to walk at 12 months, pain and quality of life. We also analysed Electrophysiological data from our local centre (Glasgow); compared newly published electrophysiological diagnostic criteria with existing criteria to determine whether serial studies are required for final electrophysiological diagnosis. Finally, to identify the patients with poor prognosis early in the disease course, we attempted to validate the currently available clinical prognostic models. Method: We conducted a multicentre prospective observational study named IGOS (International GBS Outcome Study) with a web-based entry system. It aimed to study at least 1000 patients over 3 years. The study included two modules: 1) core module which consist of a) acute clinical data collection at 0, 1, 2, 4 weeks and follow up data at 6 and 12 months b) serum samples collection at each clinical data entry point c) electrophysiology studies within 2 weeks 2) optional modules included additional electrophysiology studies at 4 weeks, CSF studies and long term outcome data at 2 and 3 years. As the study still ongoing, I analysed the data of 122 GBS patients recruited from 15 UK centres between May 2012 and Jan 2015. Results: In our cohort about 20 % patients remained disabled at 1 year, 18% required mechanical ventilation (MV), 5 % died. Pain continued to remain a major disabling symptom in more than half of the patients however unable to perform usual activity was the most disabling QoL domain affected at 12 months and was an important contributing factor affecting quality of life. Intravenous immunoglobulin was the most commonly prescribed treatment followed by plasma exchange. Immunotherapy was not beneficial in mildly affected GBS patients. Currently available electro diagnostic criteria are not very sensitive in identifying final EP subtypes and newly published Rajabally's criteria potentially addresses this issue and should be used in clinical practice to establish final EP diagnosis. Existing prognostic models EGOS and mEGOS performed well in our cohort and showed good discriminatory capacity. Discussion: Despite wider availability of immunotherapy prognosis of GBS has not changed in last 20years, which highlights the urgent need of more effective treatments in these patients. However new therapy can be expensive and can be only beneficial if the patients with poor prognosis are identified early in course. This can only be achieved by developing good prognostic models. Our results show that existing available models EGOS/MEGOS validates well and provides a proof of the concept that prognostic model can be used to identify patients with poor prognosis when the treatment is most beneficial. GBS continues to remain a clinical diagnosis. While there are drawbacks of existing EP criteria, newly developed Rajabally's criteria are sufficient to establish final EP diagnosis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (M.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.726687  DOI: Not available
Keywords: QR180 Immunology
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