Prediction of on-treatment disability worsening in RRMS with the MAGNIMS score.

BACKGROUND: The magnetic resonance imaging in multiple sclerosis (MAGNIMS) score combines relapses and magnetic resonance imaging (MRI) lesions to predict disability outcomes in relapsing-remitting multiple sclerosis (RRMS) treated with interferon-ß. OBJECTIVE: To validate the MAGNIMS score and extend to other disease-modifying therapies (DMTs). To examine the prognostic value of gadolinium contrast-enhancing (Gd+) lesions. METHODS: This RRMS MSBase cohort study (n = 2293) used a Cox model to examine the prognostic value of relapses, MRI activity and the MAGNIMS score for disability worsening during treatment with interferon-ß and three other DMTs. RESULTS: Three new T2 lesions (hazard ratio (HR) = 1.60, p = 0.028) or two relapses (HR = 2.24, p = 0.002) on interferon-ß (for 12 months) were predictive of disability worsening over 4 years. MAGNIMS score = 2 (1 relapse and ⩾3 T2 lesions or ⩾2 relapses) was associated with a greater risk of disability worsening on interferon-ß (HR = 2.0, p = 0.001). In pooled cohort of four DMTs, similar associations were seen (MAGNIMS score = 2: HR = 1.72, p = 0.001). Secondary analyses demonstrated that the addition of Gd+ to the MAGNIMS did not materially improve its prediction of disability worsening. CONCLUSION: We have validated the MAGNIMS score in RRMS and extended its application to three other DMTs: 1 relapse and ⩾3 T2 lesions or ⩾2 relapses predicted worsening of disability. Contrast-enhancing lesions did not substantially improve the prognostic score.

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2-4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement.