Hypogammaglobulinemia and Infection Risk in an Ocrelizumab-treated Multiple Sclerosis Cohort
Bibliographic record
Abstract
BACKGROUND: Ocrelizumab is an effective anti-CD20 therapy approved for Relapsing Remitting (RRMS) and Primary Progressive Multiple Sclerosis (PPMS). In clinical trials, a proportion of patients developed hypogammaglobulinemia which could contribute to infection risk. This study aimed to identify hypogammaglobulinemia and its risk factors and evaluate potentially associated serious infection risk in a real-world cohort of patients. METHODS: All MS patients treated with ocrelizumab in a Quebec City MS clinic from January 2017 to August 2021 were included and detailed patient characteristics were collected by chart review. Levels of immunoglobulins (IgM, IgA and IgG) were assessed prior to each treatment. Serious infection was defined as an infection requiring hospitalization or emergency room treatment. Association between hypogammaglobulinemia and serious infection was analyzed. RESULTS: A total of 266 patients (average follow-up 2.05 years) were included (87% RRMS). After 6 infusions, 32.8%, 3.5% and 4.2% of patients had at least one IgM, IgA and IgG hypogammaglobulinemia event respectively. Aside from pre-treatment hypogammaglobulinemia, there were no variables associated with on-treatment hypogammaglobulinemia. There was a total of 21 serious infections (3.36 and 12.33 per 100-person-years in RRMS and PPMS). Developing hypogammaglobulinemia during treatment was not associated with serious infection. A regression analysis did not show associations between serious infection and key disease characteristics. CONCLUSION: Similar to ocrelizumab extension studies, our cohort demonstrated a significant rate of hypogammaglobulinemia over time, mostly with IgM. No association was found between hypogammaglobulinemia and serious infection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".