First-year treatment response predicts the following 5-year disease course in patients with relapsing-remitting multiple sclerosis - Simona Toscano; Tim Spelman; Serkan Ozakbas; Raed Alroughani; Clara G. Chisari; Salvatore Lo Fermo; Alexandre Prat; Marc Girard; Pierre Duquette; Guillermo Izquierdo; Sara Eichau; Pierre Grammond; et al. (Neurotherapeutics)
Predicting long-term prognosis and choosing the appropriate therapeutic approach in patients with Multiple Sclerosis (MS) at the time of diagnosis is crucial in view of a personalized medicine. We investigated the impact of early therapeutic response on the 5-year prognosis of patients with relapsing-remitting MS (RRMS). We recruited patients from MSBase Registry covering the period between 1996 and 2022. All patients were diagnosed with RRMS and actively followed-up for at least 5 years to explore the following outcomes: clinical relapses, confirmed disability worsening (CDW) and improvement (CDI), EDSS 3.0, EDSS 6.0, conversion to secondary progressive MS (SPMS), new MRI lesions, Progression Independent of Relapse Activity (PIRA). Predictors included demographic, clinical and radiological data, and sub-optimal response (SR) within the first year of treatment. Female sex (HR 1.27; 95 ​% CI 1.16–1.40) and EDSS at baseline (HR 1.19; 95 ​% CI 1.15–1.24) were independent risk factors for the occurrence of relapses during the first 5 years after diagnosis, while high-efficacy treatment (HR 0.78; 95 ​% CI 0.67–0.91) and age at diagnosis (HR 0.83; 95 ​% CI 0.79–0.86) significantly reduced the risk. SR predicted clinical relapses (HR ​= ​3.84; 95 ​% CI 3.51–4.19), CDW (HR ​= ​1.74; 95 ​% CI 1.56–1.93), EDSS 3.0 (HR ​= ​3.01; 95 ​% CI 2.58–3.51), EDSS 6.0 (HR ​= ​1.77; 95 ​% CI 1.43–2.20) and new brain (HR ​= ​2.33; 95 ​% CI 2.04–2.66) and spinal (HR 1.65; 95 ​% CI 1.29–2.09) MRI lesions. This study highlights the importance of selecting the appropriate DMT for each patient soon after MS diagnosis, also providing clinicians with a practical tool able to calculate personalized risk estimates for different outcomes.