OPTIMISE: MS study protocol: a pragmatic, prospective observational study to address the need for, and challenges with, real world pharmacovigilance in multiple sclerosis

Author:

Dobson RuthORCID,Craner Matthew,Waddingham Ed,Miller Aleisha,Cavey Ana,Webb Stewart,Hemingway Cheryl,Hobart Jeremy,Evangelou Nikos,Scolding Neil,Rog David,Nicholas Richard,Marta Monica,Blain Camilla,Young Carolyn Anne,Ford Helen LORCID,Matthews Paul M

Abstract

IntroductionThe power of ‘real world’ data to improve our understanding of the clinical aspects of multiple sclerosis (MS) is starting to be realised. Disease modifying therapy (DMT) use across the UK is driven by national prescribing guidelines. As such, the UK provides an ideal country in which to gather MS outcomes data. A rigorously conducted observational study with a focus on pharmacovigilance has the potential to provide important data to inform clinicians and patients while testing the reliability of estimates from pivotal trials when applied to patients in the UK.Methods and analysisThe primary aim of this study is to characterise the incidence and compare the risk of serious adverse events in people with MS treated with DMTs. The OPTIMISE:MS database enables electronic data capture and secure data transfer. Selected clinical data, clinical histories and patient-reported outcomes are collected in a harmonised fashion across sites at the time of routine clinical visits. The first patient was recruited to the study on 24 May 2019. As of January 2021, 1615 individuals have baseline data recorded; follow-up data are being captured and will be reported in due course.Ethics and disseminationThis study has ethical permission (London City and East; Ref 19/LO/0064). Potential concerns around data storage and sharing are mitigated by the separation of identifiable data from all other clinical data, and limiting access to any identifiable data. The results of this study will be disseminated via publication. Participants provide consent for anonymised data to be shared for further research use, further enhancing the value of the study.

Funder

Celgene

Biogen IDEC Limited

Merck

Publisher

BMJ

Subject

General Medicine

Reference37 articles.

1. Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment;Cohen;Mult Scler,2020

2. Observational designs in clinical multiple sclerosis research: Particulars, practices and potentialities;Jongen;Mult Scler Relat Disord,2019

3. MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis

4. MULTILEARNING Group Inc . Big multiple sclerosis data network: marginal structural models. by Giuseppe Lucisano. Available: https://onlinelibrary.ectrims-congress.eu/ectrims/2019/stockholm/279417/giuseppe.lucisano.big.multiple.sclerosis.data.network.marginal.structural.html [Accessed 22 Apr 2020].

5. Timing of high-efficacy therapy for multiple sclerosis: a retrospective observational cohort study;He;Lancet Neurol,2020

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3