Real-world effectiveness of horizontal switching between disease-modifying therapies in multiple sclerosis: a retrospective analysis of the MSBase Registry
Bibliographic record
Abstract
Background: Switching disease-modifying therapies (DMTs) is common in relapsing-remitting multiple sclerosis (RRMS). Vertical switching to higher-efficacy agents generally outperforms horizontal switching within the same efficacy tier, yet horizontal switches remain frequent where escalation is impractical. Objectives: To compare real-world outcomes after horizontal versus vertical DMT switches and to identify predictors of successful horizontal switching. Design: Retrospective, registry-based observational study. Methods: Adults with RRMS who switched DMTs in the MSBase Registry (2010-2023) were analyzed. Horizontal switches were defined as transitions within efficacy tiers, and vertical switches as transitions to a higher tier. Propensity score matching (1:1) generated balanced cohorts. Multivariable mixed-effects models with a random intercept for patients were used to evaluate associations with outcomes. The primary outcome was no evidence of disease activity (NEDA-3) during the treatment period; secondary outcomes included annualized relapse rate (ARR), Expanded Disability Status Scale (EDSS) change, confirmed disability worsening (CDW), confirmed disability improvement (CDI), and progression independent of relapse activity (PIRA). Predictors of successful horizontal switching were explored using logistic regression. Results: A total of 4934 matched switches (2467 pairs) were analyzed. Vertical switching achieved higher NEDA-3 rates than horizontal switching (45.8% vs 33.7%) and was associated with lower ARR, reduced CDW risk, and more frequent CDI; differences in EDSS progression and PIRA were not significant. Among horizontal switchers, 33.7% achieved NEDA-3. Success was associated with lower baseline EDSS, fewer prior relapses, and later-line switching. Outcomes varied by destination therapy: anti-CD20 agents had the highest success (≈50%), followed by cladribine (≈43%) and natalizumab (≈41%), whereas interferon and glatiramer acetate performed the poorest. Switches toward anti-CD20 therapies generally yielded better outcomes than other within-tier changes. Conclusion: Vertical switching should be preferred when treatment modification is required, particularly for patients with active disease. However, a subset of patients can achieve disease stability after horizontal switching, especially those with lower disability and fewer prior relapses. The dynamics of horizontal switching may further influence outcomes, warranting prospective validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".