TY - JOUR
T1 - Prediction of relapse activity when switching to cladribine for multiple sclerosis
AU - Zhong, Michael
AU - van der Walt, Anneke
AU - Monif, Mastura
AU - Hodgkinson, Suzanne
AU - Eichau, Sara
AU - Kalincik, Tomas
AU - Lechner-Scott, Jeannette
AU - Buzzard, Katherine
AU - Skibina, Olga
AU - Van Pesch, Vincent
AU - Butler, Ernest
AU - Prevost, Julie
AU - Girard, Marc
AU - Oh, Jiwon
AU - Butzkueven, Helmut
AU - Jokubaitis, Vilija
N1 - Funding Information:
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.Z. has received research support from the Australian Government Research Training Program. A.v.d.W. reports no disclosures relevant to the manuscript. M.M. has received funding from National Medical Research Council Medical Research Future Fund (MRFF); her institution also has received funding from Brain Foundation, Charles and Sylvia Viertel Foundation, and Merck Industry funding. S.H. has received honoraria from Merck, Novartis, Roche, Biogen and Atara. S.E. received speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche and Teva. T.K. has served on scientific advisory boards for BMS, Roche, Sanofi Genzyme, Novartis, Merck and Biogen, steering committee for Brain Atrophy Initiative by Sanofi Genzyme, received conference travel support and/or speaker honoraria from WebMD Global, Novartis, Biogen, Sanofi Genzyme, Teva, BioCSL and Merck and received research or educational event support from Biogen, Novartis, Genzyme, Roche, Celgene and Merck. J.L.-S.’s institution has received speaker and advisory board fees from Biogen, Merck, Novartis and Roche. She has also received grants from Biogen, Merck and Novartis for investigator-initiated trials. K.B. received honoraria and consulting fees from Biogen, Teva, Novartis, Genzyme-Sanofi, Roche, Merck, CSL and Grifols. O.S. has worked on advisory boards of Roche, Merck and Sanofi Genzyme, received travel grants from Roche, Sanofi Genzyme and Merck, and speaker honoraria from Sanofi Genzyme, Merck and Novartis. V.V.P. has received travel grants from Merck, Biogen, Sanofi, Bristol Meyer Squibb, Almirall and Roche. His institution has received research grants and consultancy fees from Roche, Biogen, Sanofi, Merck, Bristol Meyer Squibb, Janssen Almirall and Novartis Pharma. E.B. reports no disclosures relevant to the manuscript. J.P. accepted travel compensation from Novartis, Biogen, Genzyme, Teva and speaking honoraria from Biogen, Novartis, Genzyme and Teva. M.G. received consulting fees from Teva Canada Innovation, Biogen, Novartis and Genzyme Sanofi; lecture payments from Teva Canada Innovation, Novartis and EMD; he has also received a research grant from Canadian Institutes of Health Research. J.O. has received consulting or speaking fees from Alexion, Biogen Idec, BMS, EMD Serono, Genzyme, Novartis and Roche, and has received research support from Biogen Idec, EMD Serono, and Roche. H.B. has received institutional (Monash University) funding from Biogen, F. Hoffmann-La Roche Ltd, Merck and Novartis; has carried out contracted research for Novartis, Merck, F. Hoffmann-La Roche Ltd and Biogen; has taken part in speakers’ bureau for Biogen, F. Hoffmann-La Roche Ltd, Novartis and Merck; has received personal compensation for steering committee activities from Oxford Health Policy Forum and StatFinn. V.J. received conference travel support from Merck and Roche, and speakers Honoraria from Biogen and Roche outside of the submitted work. She receives research support from the Australian National Health and Medical Research Grant and MS Research Australia.
Funding Information:
The authors thank Guillermo Izquierdo (Hospital Universitario Virgen Macarena, Sevilla, Spain), Francois Grand’Maison (Neuro-Rive-Sud, Quebec, Canada), Pamela McCombe (Royal Brisbane and Women’s Hospital, Brisbane, Australia), Serkan Ozakbas (Dokuz Eylul University, Konak/Izmir, Turkey) and Pierre Duquette and Alexandre Prat (CHUM and Universite de Montreal, Montreal, Canada) for their role in patient recruitment and data provision. The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: M.Z. received research support from the Australian Government Research Training Program during the conduct of the study.
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: M.Z. received research support from the Australian Government Research Training Program during the conduct of the study.
Publisher Copyright:
© The Author(s), 2022.
PY - 2023/1
Y1 - 2023/1
N2 - Background: Patients with relapsing–remitting multiple sclerosis commonly switch between disease-modifying therapies (DMTs). Identifying predictors of relapse when switching could improve outcomes. Objective: To determine predictors of relapse hazard when switching to cladribine. Methods: Data of patients who switched to cladribine, grouped by prior disease-modifying therapy (pDMT; interferon-β/glatiramer acetate, dimethyl fumarate, teriflunomide, fingolimod or natalizumab (NTZ)), were extracted from the MSBase Registry. Predictors of relapse hazard during the treatment gap and the first year of cladribine therapy were determined. Results: Of 513 patients, 22 relapsed during the treatment gap, and 38 within 1 year of starting cladribine. Relapse in the year before pDMT cessation predicted treatment gap relapse hazard (hazard ratio (HR) = 2.43, 95% confidence interval (CI) = 1.03–5.71). After multivariable adjustment, relapse hazard on cladribine was predicted by relapse before pDMT cessation (HR = 2.00, 95% CI = 1.01–4.02), treatment gap relapse (HR = 6.18, 95% confidence interval (CI) = 2.65–14.41), switch from NTZ (HR compared to injectable therapies 4.08, 95% CI = 1.35–12.33) and age at cladribine start (HR = 0.96, 95% CI = 0.91–0.99). Conclusion: Relapse during or prior to the treatment gap, and younger age, are of prognostic relevance in the year after switching to cladribine. Switching from NTZ is also independently associated with greater relapse hazard.
AB - Background: Patients with relapsing–remitting multiple sclerosis commonly switch between disease-modifying therapies (DMTs). Identifying predictors of relapse when switching could improve outcomes. Objective: To determine predictors of relapse hazard when switching to cladribine. Methods: Data of patients who switched to cladribine, grouped by prior disease-modifying therapy (pDMT; interferon-β/glatiramer acetate, dimethyl fumarate, teriflunomide, fingolimod or natalizumab (NTZ)), were extracted from the MSBase Registry. Predictors of relapse hazard during the treatment gap and the first year of cladribine therapy were determined. Results: Of 513 patients, 22 relapsed during the treatment gap, and 38 within 1 year of starting cladribine. Relapse in the year before pDMT cessation predicted treatment gap relapse hazard (hazard ratio (HR) = 2.43, 95% confidence interval (CI) = 1.03–5.71). After multivariable adjustment, relapse hazard on cladribine was predicted by relapse before pDMT cessation (HR = 2.00, 95% CI = 1.01–4.02), treatment gap relapse (HR = 6.18, 95% confidence interval (CI) = 2.65–14.41), switch from NTZ (HR compared to injectable therapies 4.08, 95% CI = 1.35–12.33) and age at cladribine start (HR = 0.96, 95% CI = 0.91–0.99). Conclusion: Relapse during or prior to the treatment gap, and younger age, are of prognostic relevance in the year after switching to cladribine. Switching from NTZ is also independently associated with greater relapse hazard.
KW - Disease-modifying therapies
KW - cladribine
KW - outcome measurement
KW - second-line treatment
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U2 - 10.1177/13524585221111677
DO - 10.1177/13524585221111677
M3 - Article
C2 - 35894247
AN - SCOPUS:85135047373
SN - 1352-4585
VL - 29
SP - 119
EP - 129
JO - Multiple Sclerosis
JF - Multiple Sclerosis
IS - 1
ER -