Contributions
Abstract: P736
Type: Poster
Abstract Category: Therapy - disease modifying - Tools for detecting therapeutic response
Background: Identifying predictors of treatment response is an important step in optimizing patient management in MS. Scoring systems have been used to predict long-term response to disease-modifying therapies according to relapses and MRI lesions during early treatment. Brain volume loss (BVL) is correlated with disability worsening, and the predictive value of scoring systems may be enhanced by the addition of BVL measures. Teriflunomide has shown consistent slowing of both disability and BVL vs placebo.
Objective(s): To predict probability of 12-week confirmed disability worsening in patients treated with teriflunomide in the TEMSO core (NCT00134563) and extension (NCT00803049) studies, using a scoring system based on relapses, new and enlarging T2 lesions, and a threshold for BVL.
Methods: After 1 year of teriflunomide treatment, patients (n=501) were classified for risk of disease worsening according to relapse status (0 to ≥2 relapses) and occurrence of new and enlarging T2 lesions (≤ or >3). Patients with scores of 0, 1, or 2-3, were categorized as having a low, intermediate, or high risk of poor treatment response, respectively. Patients with a score of 1 were re-categorized according to percentage BVL obtained from SIENA (structural image evaluation using normalization of atrophy) in a blinded post hoc analysis of TEMSO: category 1a, ≤−0.8; category 1b, >−0.8. Between-group comparisons were made using the log-rank test; risk reductions were based on a Cox proportional hazards model.
Results: The majority of patients (>90%) were categorized as having low (n=329) or intermediate (n=125) risk of disability worsening after 1 year of treatment. Patients in the intermediate group were re-categorized as follows: low risk, 1a (BVL ≤−0.8), n=90; high risk, 1b (BVL >−0.8), n=35. The probability of being free of disability worsening over 7 years in the TEMSO extension for patients in the low-risk groups (scores 0+1a) was significantly increased vs the high-risk groups (scores 1b+2-3); 68.5% (95% CI: 17%, 242%), P=0.0022. Over 7 years, >84% of patients receiving teriflunomide were at a low risk of disability worsening.
Conclusions: Classification of patients based on relapses and lesion activity after 1 year of teriflunomide treatment predicted the differential rate of long-term disability worsening. Additional predictive value was provided using BVL. Over 7 years, most patients receiving teriflunomide were at a low risk of disability worsening.
Disclosure: Study supported by Sanofi Genzyme.
MPS: Consulting fees (Biogen, Genzyme, Merck Serono, Novartis, Roche, Synthon, Teva).
JW: CEO of MIAC AG Basel, Switzerland; speaker honoraria (Bayer, Biogen, Novartis, Teva); advisory boards and research grants (Biogen, Novartis); supported by the German Ministry of Science (BMBF/KKNMS) and German Ministry of Economy (BMWi).
KT: Employee of Sanofi Genzyme.
ND: Consulting fees, speaking, travel support (Biogen Idec, Genzyme, Merck Serono, Novartis, Schering, Teva); advisory boards (Merck Serono, Novartis); research grant support (Italian MS Society).
Abstract: P736
Type: Poster
Abstract Category: Therapy - disease modifying - Tools for detecting therapeutic response
Background: Identifying predictors of treatment response is an important step in optimizing patient management in MS. Scoring systems have been used to predict long-term response to disease-modifying therapies according to relapses and MRI lesions during early treatment. Brain volume loss (BVL) is correlated with disability worsening, and the predictive value of scoring systems may be enhanced by the addition of BVL measures. Teriflunomide has shown consistent slowing of both disability and BVL vs placebo.
Objective(s): To predict probability of 12-week confirmed disability worsening in patients treated with teriflunomide in the TEMSO core (NCT00134563) and extension (NCT00803049) studies, using a scoring system based on relapses, new and enlarging T2 lesions, and a threshold for BVL.
Methods: After 1 year of teriflunomide treatment, patients (n=501) were classified for risk of disease worsening according to relapse status (0 to ≥2 relapses) and occurrence of new and enlarging T2 lesions (≤ or >3). Patients with scores of 0, 1, or 2-3, were categorized as having a low, intermediate, or high risk of poor treatment response, respectively. Patients with a score of 1 were re-categorized according to percentage BVL obtained from SIENA (structural image evaluation using normalization of atrophy) in a blinded post hoc analysis of TEMSO: category 1a, ≤−0.8; category 1b, >−0.8. Between-group comparisons were made using the log-rank test; risk reductions were based on a Cox proportional hazards model.
Results: The majority of patients (>90%) were categorized as having low (n=329) or intermediate (n=125) risk of disability worsening after 1 year of treatment. Patients in the intermediate group were re-categorized as follows: low risk, 1a (BVL ≤−0.8), n=90; high risk, 1b (BVL >−0.8), n=35. The probability of being free of disability worsening over 7 years in the TEMSO extension for patients in the low-risk groups (scores 0+1a) was significantly increased vs the high-risk groups (scores 1b+2-3); 68.5% (95% CI: 17%, 242%), P=0.0022. Over 7 years, >84% of patients receiving teriflunomide were at a low risk of disability worsening.
Conclusions: Classification of patients based on relapses and lesion activity after 1 year of teriflunomide treatment predicted the differential rate of long-term disability worsening. Additional predictive value was provided using BVL. Over 7 years, most patients receiving teriflunomide were at a low risk of disability worsening.
Disclosure: Study supported by Sanofi Genzyme.
MPS: Consulting fees (Biogen, Genzyme, Merck Serono, Novartis, Roche, Synthon, Teva).
JW: CEO of MIAC AG Basel, Switzerland; speaker honoraria (Bayer, Biogen, Novartis, Teva); advisory boards and research grants (Biogen, Novartis); supported by the German Ministry of Science (BMBF/KKNMS) and German Ministry of Economy (BMWi).
KT: Employee of Sanofi Genzyme.
ND: Consulting fees, speaking, travel support (Biogen Idec, Genzyme, Merck Serono, Novartis, Schering, Teva); advisory boards (Merck Serono, Novartis); research grant support (Italian MS Society).