Literature DB >> 26810535

Short-term suboptimal response criteria for predicting long-term non-response to first-line disease modifying therapies in multiple sclerosis: A systematic review and meta-analysis.

Jordi Río1, Juan Luís Ruiz-Peña2.   

Abstract

INTRODUCTION: There is no consensus about short-term suboptimal response to first-line treatments in relapsing-remitting multiple sclerosis.
METHODS: We searched studies with interferon beta or glatiramer acetate in which a long-term (≥ 2 years (y)) outcome could be predicted using short-term (≤ 1 y) suboptimal response criteria (EDSS-, imaging- and/or relapse-based). We obtained pooled diagnostic accuracy parameters for the 1-y criteria used to predict disability progression between 2-5 y.
RESULTS: We selected 45 articles. Eight studies allowed calculating pooled estimates of 16 criteria. The three criteria with best accuracy were: new or enlarging T2-weighted lesions (newT2) ≥ 1 (pooled sensitivity: 85.5%; specificity:70.2%; positive predictive value:48.0%; negative predictive value:93.8%), newT2 ≥ 2 (62.4%, 83.6%, 55.0% and 87.3%, respectively) and RIO score ≥ 2 (55.8%, 84.4%, 47.8% and 88.2%). Pooled percentages of suboptimal responders were 43.3%, 27.6% and 23.7%, respectively. Pooled diagnostic odds ratios were 14.6 (95% confidence interval: 1.4-155), 9.2 (1.4-59.0) and 8.2 (3.5-19.2).
CONCLUSIONS: All criteria had a limited predictive value. RIO score ≥ 2 at 1-y combined fair accuracy and consistency, limiting the probability of disability progression in the next years to 1 in 8 optimal responders. NewT2 ≥ 1 at 1-y had similar positive predictive value, but diminished the false negatives to 1 in 16 patients. More sensitive measures of treatment failure at short term are needed.

Entities:  

Keywords:  Disability; Glatiramer acetate; Interferon beta; Multiple sclerosis; Prediction; Suboptimal response

Mesh:

Substances:

Year:  2015        PMID: 26810535     DOI: 10.1016/j.jns.2015.12.043

Source DB:  PubMed          Journal:  J Neurol Sci        ISSN: 0022-510X            Impact factor:   3.181


  7 in total

1.  Predicting therapeutic response to fingolimod treatment in multiple sclerosis patients.

Authors:  Bibiana Quirant-Sánchez; José V Hervás-García; Aina Teniente-Serra; Luis Brieva; Ester Moral-Torres; Antonio Cano; Elvira Munteis; María J Mansilla; Silvia Presas-Rodriguez; Juan Navarro-Barriuso; Cristina Ramo-Tello; Eva M Martínez-Cáceres
Journal:  CNS Neurosci Ther       Date:  2018-04-15       Impact factor: 5.243

2.  Immunotherapy for people with clinically isolated syndrome or relapsing-remitting multiple sclerosis: treatment response by demographic, clinical, and biomarker subgroups (PROMISE)-a systematic review protocol.

Authors:  Thomas Lehnert; Christian Röver; Sascha Köpke; Jordi Rio; Declan Chard; Andrea V Fittipaldo; Tim Friede; Christoph Heesen; Anne C Rahn
Journal:  Syst Rev       Date:  2022-07-01

3.  Efficacy and safety of ocrelizumab in patients with relapsing-remitting multiple sclerosis with suboptimal response to prior disease-modifying therapies: A primary analysis from the phase 3b CASTING single-arm, open-label trial.

Authors:  Patrick Vermersch; Celia Oreja-Guevara; Aksel Siva; Bart Van Wijmeersch; Heinz Wiendl; Jens Wuerfel; Regine Buffels; Karen Kadner; Thomas Kuenzel; Giancarlo Comi
Journal:  Eur J Neurol       Date:  2021-11-25       Impact factor: 6.288

4.  Assessing the effect of an evidence-based patient online educational tool for people with multiple sclerosis called UMIMS-understanding magnetic resonance imaging in multiple sclerosis: study protocol for a double-blind, randomized controlled trial.

Authors:  Insa Schiffmann; Magalie Freund; Eik Vettorazzi; Jan-Patrick Stellmann; Susanne Heyer-Borchelt; Marie D'Hooghe; Vivien Häußler; Anne Christin Rahn; Christoph Heesen
Journal:  Trials       Date:  2020-12-09       Impact factor: 2.279

5.  Efficacy of Cladribine Tablets in high disease activity subgroups of patients with relapsing multiple sclerosis: A post hoc analysis of the CLARITY study.

Authors:  Gavin Giovannoni; Per Soelberg Sorensen; Stuart Cook; Kottil W Rammohan; Peter Rieckmann; Giancarlo Comi; Fernando Dangond; Christine Hicking; Patrick Vermersch
Journal:  Mult Scler       Date:  2018-05-02       Impact factor: 6.312

6.  The Effect of Disease Modifying Therapies on Disability Progression in Multiple Sclerosis: A Systematic Overview of Meta-Analyses.

Authors:  Suzi B Claflin; Simon Broadley; Bruce V Taylor
Journal:  Front Neurol       Date:  2019-01-10       Impact factor: 4.003

7.  Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis.

Authors:  Ruggiero Seccia; Daniele Gammelli; Fabio Dominici; Silvia Romano; Anna Chiara Landi; Marco Salvetti; Andrea Tacchella; Andrea Zaccaria; Andrea Crisanti; Francesca Grassi; Laura Palagi
Journal:  PLoS One       Date:  2020-03-20       Impact factor: 3.240

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.