Low Risk of Hepatitis B Virus Reactivation in HBsAg-negative/Anti-HBc–positive Carriers Receiving Rituximab for Rheumatoid Arthritis: A Retrospective Multicenter Italian Study
Bibliographic record
Abstract
OBJECTIVE: Patients with resolved hepatitis B virus (HBV) infection, i.e., hepatitis B surface antigen (HBsAg)-negative/antihepatitis B core antigen (anti-HBc)-positive, undergoing rituximab (RTX)-based chemotherapy for hematological malignancies without anti-HBV prophylaxis are at risk of HBV reactivation, but the risk in such patients receiving RTX for rheumatological disorders is not clear. We evaluated this risk in HBsAg-negative/anti-HBc-positive patients with rheumatoid arthritis (RA) undergoing RTX without prophylaxis. METHODS: Thirty-three HBsAg-negative/anti-HBc-positive outpatients with RA with undetectable HBV DNA by sensitive PCR assay [73% women, median age 60 years, 85% with HBsAg antibodies (anti-HBs), 37% with antihepatitis B envelope antigen] received a median of 3 cycles of RTX (range 1-8) over 34 months (range 0-80) combined with disease-modifying antirheumatic drugs (DMARD) without prophylaxis. All underwent clinical and laboratory monitoring during and after RTX administration, including serum HBsAg and HBV DNA measurements every 6 months or whenever clinically indicated. RESULTS: None of the patients seroreverted to HBsAg during RTX treatment, but 6/28 (21%) showed a > 50% decrease in protective anti-HBs levels, including 2 who became anti-HBs-negative. One patient (3%) who became HBV DNA-positive (44 IU/ml) after 6 months of RTX treatment was effectively rescued with lamivudine before any hepatitis flare occurred. Among the 14 patients monitored for 18 months (range 0-70) after RTX discontinuation, no HBV reactivation was observed. CONCLUSION: The administration of RTX + DMARD in patients with RA with resolved HBV infection leads to a negligible risk of HBV reactivation, thus suggesting that serum HBsAg and/or HBV DNA monitoring but not universal anti-HBV prophylaxis is justified.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".