Real-world change in annualized relapse rate and healthcare resource utilization following initiation of ofatumumab in people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Ofatumumab (OMB) is an FDA-approved CD20-directed monoclonal antibody with demonstrated efficacy in reducing incidence of relapse in multiple sclerosis (MS). Real-world data are needed to ascertain OMB's effectiveness in reducing relapse in a broader MS population and to assess whether clinical benefits of OMB translate into decreased healthcare resource utilization (HCRU). The current study utilized a large US administrative claims database to compare relapse and MS-related HCRU before and after initiation of OMB therapy in a real-world sample of people with MS. METHODS: A retrospective pre-post cohort study was conducted using Optum® Clinformatics® Data Mart database (August 2019-December 2023). Adults with an MS diagnosis initiated on OMB (index date) between August 2020 (FDA approval date) and July 2023 were included. Patients were required to be continuously enrolled in a healthcare plan ≥12 months before and ≥6 months after index date and persistent on OMB, defined as no gaps in treatment ≥60 days or treatment switch, for ≥6 months following index date. Relapse was defined using a validated claims-based algorithm. MS-related HCRU included hospitalizations, days of hospitalization, emergency department (ED) visits, and outpatient (OP) visits. The study period was divided into a 12-month pre- (before OMB initiation) and ≥6-month post-index period (from OMB initiation until end of follow-up or persistent OMB use). Rates of relapse and MS-related HCRU per person-year (PPY) were assessed using negative binomial regression and compared between pre- and post-index periods using unadjusted incidence rate ratios (IRRs). RESULTS: In 779 included patients, mean (standard deviation) age at index was 48 (11) years, 74 % were female, and 69 % were White, with a mean follow-up of 1.36 years. In the pre-index period, 42 % and 23 % of patients received low-/moderate- and high-efficacy disease-modifying therapies, respectively. Annualized relapse rate (ARR) (95 % confidence interval [CI]; N relapse episodes/N person-years) in the pre-index period was 0.41 (0.36-0.47; 317/779) compared with 0.10 (0.08-0.13; 103/1060) in the post-index period. This equated to a statistically significant 75 % reduction in ARR following OMB initiation (IRR 0.25; 95 % CI, 0.20-0.31; p < 0.001). Hospitalizations PPY (95 % CI) decreased significantly from 0.16 (0.13-0.21) to 0.02 (0.01-0.03; IRR 0.10; 95 % CI, 0.06-0.18; p < 0.001). Similarly, days of hospitalization decreased significantly from 0.39 (0.20-0.75) to 0.12 (0.10-0.14; IRR 0.36; p = 0.004). OP visits also decreased significantly from 6.56 (6.21-6.92) to 4.60 (4.36-4.85; IRR 0.70; p < 0.001), whereas ED visits PPY decreased non-significantly from 0.16 (0.12-0.22) to 0.13 (0.10-0.18; IRR 0.79; p = 0.153). Significant reductions in ARR and MS-related hospitalization, days of hospitalization, and OP visits were observed regardless of whether patients were required to be persistent on OMB post-index for 3, 6, or 12 months. CONCLUSION: In a real-world sample of people with MS, ARR was reduced by 75 % following initiation of OMB. MS-related hospitalization, days of hospitalization, and OP visits also decreased significantly following OMB initiation. Results align with clinical trial evidence of OMB's efficacy in reducing relapse incidence in MS and suggest these benefits translate to reduced HCRU.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".