Biologic Disease-modifying Antirheumatic Drug (bDMARD)-induced Neutropenia: A Registry from a Retrospective Cohort of Patients with Rheumatic Diseases Treated with 3 Classes of Intravenous bDMARD
Bibliographic record
Abstract
OBJECTIVE: To examine the rate, risks factors, and consequences of neutropenia induced by intravenous (IV) biologic disease-modifying antirheumatic drugs (bDMARD). METHODS: We conducted a retrospective cohort study in 499 patients with rheumatic diseases treated by IV abatacept (ABA), infliximab (IFX), or tocilizumab (TCZ). RESULTS: Rheumatoid arthritis (RA) was the most frequent diagnosis (72%). Fifty-two patients (10.4%) experienced at least 1 episode of neutropenia. No episodes of grade 4 neutropenia were documented. TCZ was more frequently related to neutropenia than ABA or IFX (18.6% vs 3.8% and 2.8%, respectively, p < 0.001). The following factors were identified as predictors of experiencing neutropenia with IV bDMARD: history of neutropenia with methotrexate (MTX; synthetic DMARD; OR 1.56, 95% CI 1.17-7.14), concomitant treatment by MTX (OR 1.21, 95% CI 1.01-2.64), and TCZ treatment (OR 2.72, 95% CI 1.53-9.05). Patients experiencing a TCZ-induced neutropenia did not show a higher risk of severe infections; however, this group had a shorter drug survival (9 mos vs 20 mos, p < 0.02) compared with TCZ patients without neutropenia. CONCLUSION: Among 3 different classes of IV bDMARD, TCZ is associated with the higher risk of neutropenia. No increased frequency of infection episodes was documented in this 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".