Open Access

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about 2.8 million people worldwide. Disease course after the most common diagnoses of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) is highly variable and cannot be reliably predicted. This impairs early personalized treatment decisions. Objectives: The main objective of this study was to algorithmically support clinical decision-making regarding the options of early platform medication or no immediate treatment of patients with early RRMS and CIS. Design: Retrospective monocentric cohort study within the Data Integration for Future Medicine (DIFUTURE) Consortium. Methods: Multiple data sources of routine clinical, imaging and laboratory data derived from a large and deeply characterized cohort of patients with MS were integrated to conduct a retrospective study to create and internally validate a treatment decision score [Multiple Sclerosis Treatment Decision Score (MS-TDS)] through model-based random forests (RFs). The MS-TDS predicts the probability of no new or enlarging lesions in cerebral magnetic resonance images (cMRIs) between 6 and 24 months after the first cMRI. Results: Data from 65 predictors collected for 475 patients between 2008 and 2017 were included. No medication and platform medication were administered to 277 (58.3%) and 198 (41.7%) patients. The MS-TDS predicted individual outcomes with a cross-validated area under the receiver operating characteristics curve (AUROC) of 0.624. The respective RF prediction model provides patient-specific MS-TDS and probabilities of treatment success. The latter may increase by 5–20% for half of the patients if the treatment considered superior by the MS-TDS is used. Conclusion: Routine clinical data from multiple sources can be successfully integrated to build prediction models to support treatment decision-making. In this study, the resulting MS-TDS estimates individualized treatment success probabilities that can identify patients who benefit from early platform medication. External validation of the MS-TDS is required, and a prospective study is currently being conducted. In addition, the clinical relevance of the MS-TDS needs to be established.

Objective: This study analyzed clinical characteristics, attack recovery and long-term disability accumulation in late-onset (LO ≥ 50 years at onset) versus early-onset (EO < 50 years) NMOSD. Methods: This multicenter cohort study included demographic and clinical data from 446 NMOSD patients collected from 35 German Neuromyelitis Optica Study Group (NEMOS) centers. Time to disability milestones was estimated through Kaplan-Meier analysis and Cox proportional hazard regression models adjusted for sex, year of onset, immunotherapy exposure and antibody status. Generalized estimating equations (GEE) were used to compare attack outcomes. Results: Of the 446 NMOSD patients analyzed (83.4% female, 85.4% AQP4-IgG-positive, median age at disease onset = 43 years), 153 had a late-onset (34.3%). AQP4-IgG+ prevalence was higher in LO- than in EO-NMOSD (94.1% vs. 80.9%, p<0.001). Optic neuritis at onset was more frequent in EO-NMOSD (27.4% vs. 42.6%, p<0.002), whereas myelitis was more common in LO-NMOSD (58.4% vs. 37.9%, p<0.001). Both groups had similar annualized attack rates (AAR, 0.51 vs. 0.54, p=0.352), but attack recovery was poorer (complete remission in 15.6% vs. 27.4%, p<0.001) and relapse-associated worsening (RAW) was higher in LO-NMOSD (RAW: 3 vs. 0.5, p<0.001). Long-term immunotherapy use was comparable. LO-NMOSD exhibited faster progression to disability endpoints (EDSS 4: HR = 2.64, 95% CI=1.81–3.84). Interpretation: LO-NMOSD patients presented more often with myelitis, experienced worse attack outcomes and faster disability accumulation, despite comparable AAR, acute attack treatment and long-term treatment regimens. Accordingly, therapeutic strategies for attack and prophylactic treatment in LO-NMOSD have to be improved.