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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".