Comparing ocrelizumab to interferon/glatiramer acetate in people with multiple sclerosis over age 60
Bibliographic record
Abstract
BACKGROUND: Ongoing controversy exists regarding optimal management of disease modifying therapy (DMT) in older people with multiple sclerosis (pwMS). There is concern that the lower relapse rate, combined with a higher risk of DMT-related infections and side effects, may alter the risk-benefit balance in older pwMS. Given the lack of pwMS above age 60 in randomised controlled trials, the comparative efficacy of high-efficacy DMTs such as ocrelizumab has not been shown in older pwMS. We aimed to evaluate the comparative effectiveness of ocrelizumab, a high-efficacy DMT, versus interferon/glatiramer acetate (IFN/GA) in pwMS over the age of 60. METHODS: Using data from MSBase registry, this multicentre cohort study included pwMS above 60 who switched to or started on ocrelizumab or IFN/GA. We analysed relapse and disability outcomes after balancing covariates using an inverse probability treatment weighting (IPTW) method. Propensity scores were obtained based on age, country, disease duration, sex, baseline Expanded Disability Status Scale, prior relapses (all-time, 12 months and 24 months) and prior DMT exposure (overall number and high-efficacy DMTs). After weighting, all covariates were balanced. Primary outcomes were time to first relapse and annualised relapse rate (ARR). Secondary outcomes were 6-month confirmed disability progression (CDP) and confirmed disability improvement (CDI). RESULTS: A total of 248 participants received ocrelizumab, while 427 received IFN/GA. The IPTW-weighted ARR for ocrelizumab was 0.01 and 0.08 for IFN/GA. The IPTW-weighted ARR ratio was 0.15 (95% CI 0.06 to 0.33, p<0.001) for ocrelizumab compared with IFN/GA. On IPTW-weighted Cox regression models, HR for time to first relapse was 0.13 (95% CI 0.05 to 0.26, p<0.001). The hazard of first relapse was significantly reduced in ocrelizumab users after 5 months compared with IFN/GA users. However, the two groups did not differ in CDP or CDI over 3.57 years. CONCLUSION: In older pwMS, ocrelizumab effectively reduced relapses compared with IFN/GA. Overall relapse activity was low. This study adds valuable real-world data for informed DMT decision making with older pwMS. Our study also confirms that there is a treatment benefit in older people with MS, given the existence of a clear differential treatment effect between ocrelizumab and IFN/GA in the over 60 age group.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".