Risk of Hepatitis B Virus Reactivation in Patients With Inflammatory Arthritis Receiving Disease‐Modifying Antirheumatic Drugs: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To assess hepatitis B virus (HBV) reactivation rates in patients with resolved or chronic HBV infection, receiving disease-modifying antirheumatic drugs (DMARDs) and with or without antiviral prophylaxis. METHODS: We conducted a systematic review and meta-analysis. Electronic searches were conducted in PubMed, Medline, and Embase using Ovid through December 31, 2015. A search strategy was developed for each database using the following inclusion criteria: for participants, rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, and resolved or chronic HBV infection; for intervention, tumor necrosis factor (TNF) inhibitors or non-TNF biologic or nonbiologic DMARDs; and for outcome, HBV reactivation. Four reviewers independently extracted study data and assessed study quality using the Newcastle-Ottawa Scale. To determine the pooled HBV reactivation rate, the variances of the raw proportions were stabilized using a Freeman-Tukey-type arcsine square root transformation, using a random-effects model. RESULTS: Twenty-five studies met the inclusion criteria. The overall pooled rate of HBV reactivation was 1.6% (95% confidence interval [95% CI] 0.8-2.6) in patients with resolved HBV. Similar rates were observed in resolved patients taking TNF inhibitors (1.4% [95% CI 0.5-2.6]), non-TNF biologics (6.1% [95% CI 0.0-16.6]), and nonbiologic DMARDs (1.7% [95% CI 0.2-4.2]). We also found that the reactivation rate was lower in patients with chronic HBV infection who received antiviral prophylaxis (9.0% [95% CI 4.1-15.5]) than in those who did not (14.6% [95% CI 4.3-29.0]). CONCLUSION: We found that the HBV reactivation rate in inflammatory arthritis patients receiving DMARDs was low in resolved patients and moderate in patients with chronic HBV infection. Further, lower rates were observed in patients with chronic HBV infection who were using antiviral prophylaxis.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".