Contributions
Abstract: EP1637
Type: ePoster
Abstract Category: Therapy - disease modifying - 26 Immunomodulation/Immunosuppression
Background: Fingolimod has been used in ~204,000 RRMS patients (pts) having a total pt exposure >424,000 pt-years. Analysis of clinical practice data is key to make accurate treatment decisions.
Objective: To describe effectiveness variables according to clinical and demographic characteristics in RRMS pts.
Methods: Pooled analysis of 2 observational, retrospective, multicentre Spanish studies (MS-Second Line Gate and MS-Next) in ≥18 years RRMS pts treated with fingolimod 0.5mg/day and ≥1 year follow-up after treatment initiation in clinical practice (Nov2014-Dec2015). Regression models were performed to identify potential predictive variables of the number of relapses and Expanded Disability Status Scale (EDSS) score after 1 year of fingolimod treatment using clinical and demographic characteristics at treatment initiation.
Results: 988 pts (MS-Next=804,MS-Second Line Gate=184) were analyzed: 69% female, mean age=40 years, post first-line injectable disease-modifying treatment (iDMT)=666, post-natalizumab (NTZ)=252, treatment-naïve=70. The independent predictors of the number of relapses were prior treatment (50% lower in post-iDMT/post-NTZ pts; 65% lower in treatment-naïve/post-NTZ pts), number of relapses in the previous year (32% higher per each additional relapse in the previous year), number of prior treatments (35% lower in pts with ≤1/>1) and number of T2 lesions (34% lower in pts with 9-20/>20 T2 lesions)(p< 0.05 all cases). The independent predictors of EDSS score were prior treatment (-0.39 points in post-iDMT/post-NTZ pts; -0.34 points in treatment-naïve/post-NTZ pts), age (+0.008 points per each additional year), EDSS score at treatment initiation (+0.89 points per each additional EDSS point) and time from RRMS diagnosis (+0.02 points per each additional year)(p< 0.05 all cases).
Conclusions: The prior treatment, number of relapses in the previous year, number of previous treatments, number of T2 lesions, age, RRMS evolution and EDSS score at treatment initiation should be taken into account before fingolimod treatment initiation since they might predict the clinical disease activity during the first year of treatment.
Disclosure: S. Martinez Yélamos: Sergio Martínez-Yélamos received honoraria compensation to participate in advisory boards, collaborations as a consultant and scientific communications and received research support, funding for travel and congress expenses from Biogen Idec, Novartis, TEVA, MerckSerono, Genyme and Almirall
J. Mallada:Ha recibido honorarios de Merck, Bayer, Teva, Sanofi, Novartis, Biogen y Roche
V. Meca-Lallana:Dr. V. Meca Lallana, has received honoraria and travel expenses for scientific meetings and has participated in advisory boards in the past years with: Almirall, Biogen Idec, Genzyme, Merck Serono, Novartis, Roche, TEVA and Terumo
J. Meca-Lallana:Nothing to disclosure
M. Martínez:Nothing to disclosure
E. Marzo:Eugenia Marzo has received compensation for travel expenses and speaking honoraria from Biogen Idec, Novartis and Genzyme.
C. Duran: I've received honoraria from Abbvie, Biogen, Novartis, Merck y Sanofi Genzyne
T. Ayuso:T. Ayuso has received compensation for travel expenses, speaking honoraria and consultation fees from Almirall, Biogen, Genzyme, Merck Serono, Novartis and Teva
F. Barrero:Francisco Barrero has received consulting/speaker fees from and advisory board for Bayer HealthCare, Biogen, Genzyme, Merck Serono, Novartis, Roche, Sanofi-Aventis, and Teva, and been involved with clinical trials for Novartis.
R. Romero:Novartis employee
R. Guillen:Novartis employee
J.Ricart:Novartis employee
E. Garcia:Novartis employee
Abstract: EP1637
Type: ePoster
Abstract Category: Therapy - disease modifying - 26 Immunomodulation/Immunosuppression
Background: Fingolimod has been used in ~204,000 RRMS patients (pts) having a total pt exposure >424,000 pt-years. Analysis of clinical practice data is key to make accurate treatment decisions.
Objective: To describe effectiveness variables according to clinical and demographic characteristics in RRMS pts.
Methods: Pooled analysis of 2 observational, retrospective, multicentre Spanish studies (MS-Second Line Gate and MS-Next) in ≥18 years RRMS pts treated with fingolimod 0.5mg/day and ≥1 year follow-up after treatment initiation in clinical practice (Nov2014-Dec2015). Regression models were performed to identify potential predictive variables of the number of relapses and Expanded Disability Status Scale (EDSS) score after 1 year of fingolimod treatment using clinical and demographic characteristics at treatment initiation.
Results: 988 pts (MS-Next=804,MS-Second Line Gate=184) were analyzed: 69% female, mean age=40 years, post first-line injectable disease-modifying treatment (iDMT)=666, post-natalizumab (NTZ)=252, treatment-naïve=70. The independent predictors of the number of relapses were prior treatment (50% lower in post-iDMT/post-NTZ pts; 65% lower in treatment-naïve/post-NTZ pts), number of relapses in the previous year (32% higher per each additional relapse in the previous year), number of prior treatments (35% lower in pts with ≤1/>1) and number of T2 lesions (34% lower in pts with 9-20/>20 T2 lesions)(p< 0.05 all cases). The independent predictors of EDSS score were prior treatment (-0.39 points in post-iDMT/post-NTZ pts; -0.34 points in treatment-naïve/post-NTZ pts), age (+0.008 points per each additional year), EDSS score at treatment initiation (+0.89 points per each additional EDSS point) and time from RRMS diagnosis (+0.02 points per each additional year)(p< 0.05 all cases).
Conclusions: The prior treatment, number of relapses in the previous year, number of previous treatments, number of T2 lesions, age, RRMS evolution and EDSS score at treatment initiation should be taken into account before fingolimod treatment initiation since they might predict the clinical disease activity during the first year of treatment.
Disclosure: S. Martinez Yélamos: Sergio Martínez-Yélamos received honoraria compensation to participate in advisory boards, collaborations as a consultant and scientific communications and received research support, funding for travel and congress expenses from Biogen Idec, Novartis, TEVA, MerckSerono, Genyme and Almirall
J. Mallada:Ha recibido honorarios de Merck, Bayer, Teva, Sanofi, Novartis, Biogen y Roche
V. Meca-Lallana:Dr. V. Meca Lallana, has received honoraria and travel expenses for scientific meetings and has participated in advisory boards in the past years with: Almirall, Biogen Idec, Genzyme, Merck Serono, Novartis, Roche, TEVA and Terumo
J. Meca-Lallana:Nothing to disclosure
M. Martínez:Nothing to disclosure
E. Marzo:Eugenia Marzo has received compensation for travel expenses and speaking honoraria from Biogen Idec, Novartis and Genzyme.
C. Duran: I've received honoraria from Abbvie, Biogen, Novartis, Merck y Sanofi Genzyne
T. Ayuso:T. Ayuso has received compensation for travel expenses, speaking honoraria and consultation fees from Almirall, Biogen, Genzyme, Merck Serono, Novartis and Teva
F. Barrero:Francisco Barrero has received consulting/speaker fees from and advisory board for Bayer HealthCare, Biogen, Genzyme, Merck Serono, Novartis, Roche, Sanofi-Aventis, and Teva, and been involved with clinical trials for Novartis.
R. Romero:Novartis employee
R. Guillen:Novartis employee
J.Ricart:Novartis employee
E. Garcia:Novartis employee