Ocrelizumab versus Natalizumab in Relapsing-Remitting Multiple Sclerosis: A Registry-Linked Electronic Health Records Study
Bibliographic record
Abstract
BACKGROUND: Ocrelizumab and natalizumab are commonly prescribed high-effectiveness disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (RRMS). However, no randomized clinical trial and few real-world studies have directly compared their effectiveness in reducing disability progression. Subtype classification and disability status are critical for multiple sclerosis (MS) research, but these data are often missing in electronic health records (EHRs), limiting robust real-world evidence generation. OBJECTIVE: To compare the effectiveness of ocrelizumab and natalizumab in two-year rater-assessed disability progression among RRMS patients using longitudinal registry-linked EHR data. DESIGN: Retrospective cohort study. SETTING: A large healthcare system that includes both academic and community practices. PARTICIPANTS: Patients diagnosed with MS who initiated ocrelizumab or natalizumab between 2012 and 2020, with at least 6-month EHR data before treatment initiation and no prior exposure to other high-effectiveness DMTs. EXPOSURES: Treatment with ocrelizumab vs natalizumab. MEASUREMENTS: We developed an ensemble machine learning model to impute RRMS subtype and disability outcomes using structured and narrative EHR data. The primary outcome was moderate/severe rater-assessed disability at 2 years (observed or imputed Expanded Disability Status Scale [EDSS]≥4) after treatment initiation. We estimated the average treatment effects using semi-supervised doubly robust approach with comprehensive confounder adjustment and calibration to mitigate imputation bias. Covariates included standard demographic and clinical features such as baseline disability as well as knowledge graph-selected features. Sensitivity analyses used observed EDSS scores in registry-derived RRMS patients. Exploratory analyses included rituximab, another B-cell-depleting therapy, with adjustments for differences in patient profiles. RESULTS: Among RRMS patients, those treated with ocrelizumab (n=543) had a significantly lower two-year risk of moderate/severe disability compared with those treated with natalizumab (n=205) based on imputed outcomes (risk difference, -5.87%; 95% CI: -11.28% to -0.46%; p=0.033) after confounder adjustment. Sensitivity analyses yielded consistent findings using imputed or observed EDSS outcomes in registry-derived RRMS patients. CONCLUSION AND RELEVANCE: In this real-world comparative effectiveness study using a novel semi-supervised doubly-robust framework, ocrelizumab was associated with a lower risk of disability progression than natalizumab among RRMS patients. This approach provides a roadmap for generating robust large-scale real-world evidence in settings of missing key inclusion features and outcomes.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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".