RESILIENCE (Retrospective Linkage Study of Autoimmune Encephalitis): protocol for an Australian retrospective cohort study of outcomes in autoimmune encephalitis using data linkage techniques

IntroductionThe autoimmune encephalitides (AE) are a heterogeneous group of neurological disorders with significant morbidity and healthcare costs. Despite advancements in understanding their pathophysiology, uncertainties persist regarding long-term prognosis and optimal management. This study aims...

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Published inBMJ open Vol. 14; no. 12; p. e084664
Main Authors Halliday, Amy Jean, Lambert, Katrina, Bundell, Christine, McLean-Tooke, Andrew, Gillis, David, Prain, Kerri M, Bryson, Greg, Gillinder, Lisa, Brown, David, Ramanathan, Sudarshini, Dale, Russell, Brilot, Fabienne, Jordan, Nerissa, Lawn, Nicholas, Lai, Alan, Boyd, James, Camacho, Ximena, D’Souza, Wendyl Jude, Cook, Mark, Dunne, John, Seneviratne, Udaya, Whitham, Emma, Pang, Elaine, Monif, Mastura, Douglass, Saxon, Butler, Ernest, D’Souza, Wendyl
Format Journal Article
LanguageEnglish
Published England British Medical Journal Publishing Group 05.12.2024
BMJ Publishing Group LTD
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ISSN2044-6055
2044-6055
DOI10.1136/bmjopen-2024-084664

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Summary:IntroductionThe autoimmune encephalitides (AE) are a heterogeneous group of neurological disorders with significant morbidity and healthcare costs. Despite advancements in understanding their pathophysiology, uncertainties persist regarding long-term prognosis and optimal management. This study aims to address these gaps, focusing on immunotherapeutic strategies, neoplastic associations and functional outcomes.Methods and analysisThe Retrospective Linkage Study of Autoimmune Encephalitis project will use data linkage techniques to establish a retrospective 10-year population cohort of Australian patients with AE. Two cohorts will be analysed, the Reference Cohort (clinically confirmed AE cases obtained from hospital medical records, n=145) and the Operationally Defined Cohort (AE cases identified through administrative coding data, n≈5000). Univariate statistical methods will identify candidate coding elements for use in the operational case definition and multivariate models and evaluation methods used to identify and internally validate the optimal coding algorithms. The two study cohorts will be analysed separately due to the high likelihood of overlap. Primary outcomes include relapse rate, prevalence and control of epilepsy, cognitive disability, poor educational attainment, delayed tumour diagnosis and mortality. Statistical analyses, including random mixed-effects regression models, will assess treatment effects, covariates and outcomes.Ethics and disseminationThis project has been approved by the leading investigators’ institutional Human Research Ethics Committee (HREC), the St Vincent’s Hospital Melbourne HREC, as well as the Australian Institute of Health and Welfare HREC and relevant jurisdictional HRECs where required. The dissemination of findings through peer-reviewed publications and patient advocacy channels will maximise the impact of this research.
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XC and WJD’S are joint senior authors.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
None declared.
ISSN:2044-6055
2044-6055
DOI:10.1136/bmjopen-2024-084664