Treatment De-escalation in Relapsing-Remitting Multiple Sclerosis: An Observational Study
Bibliographic record
Abstract
BACKGROUND: In relapsing-remitting multiple sclerosis (RRMS), extended exposure to high-efficacy disease modifying therapy may increase the risk of side effects, compromise treatment adherence, and inflate medical costs. Treatment de-escalation, here defined as a switch to a lower efficacy therapy, is often considered by patients and physicians, but evidence to guide such decisions is scarce. In this study, we aimed to compare clinical outcomes between patients who de-escalated therapy versus those who continued their therapy. METHODS: In this retrospective analysis of data from an observational, longitudinal cohort of 87,239 patients with multiple sclerosis (MS) from 186 centers across 43 countries, we matched treatment episodes of adult patients with RRMS who underwent treatment de-escalation from either high- to medium-, high- to low-, or medium- to low-efficacy therapy with counterparts that continued their treatment, using propensity score matching and incorporating 11 variables. Relapses and 6-month confirmed disability worsening were assessed using proportional and cumulative hazard models. RESULTS: Matching resulted in 876 pairs (de-escalators: 73% females, median [interquartile range], age 40.2 years [33.6, 48.8], Expanded Disability Status Scale [EDSS] 2.5 [1.5, 4.0]; non-de-escalators: 73% females, age 40.8 years [35.5, 47.9], and EDSS 2.5 [1.5, 4.0]), with a median follow-up of 4.8 years (IQR 3.0, 6.8). Patients who underwent de-escalation faced an increased hazard of future relapses (hazard ratio 2.36 and 95% confidence intervals [CI] [1.79-3.11], p < 0.001), which was confirmed when considering recurrent relapses (2.43 [1.97-3.00], p < 0.001). It was also consistent across subgroups stratified by age, sex, disability, disease duration, and time since last relapse. CONCLUSIONS: On the basis of this observational analysis, de-escalation may not be recommended as a universal treatment strategy in RRMS. The decision to de-escalate should be considered on an individual basis, as its safety is not clearly guided by specific patient or disease characteristics evaluated in this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".