Hepatitis B reactivation in patients with lymphoma: A meta-analysis.
Bibliographic record
Abstract
228 Background: Patients with hepatitis B virus (HBV) who are HBsAg+ are at risk of HBV reactivation if rituximab is administered in the absence of antiviral treatment. Recently, it has been reported that patients with so-called “resolved HBV infection”(HBsAg-/cAb+) may also be at risk; however, the degree of risk is not known. Methods: We performed a systematic review of the English and Chinese language literature in Medline (1996 to June week 3 2013) and Embase (1996 to 2013 week 26) using the MeSH terms “lymphoma” and “hepatitis B”. Eligible studies were limited to those reporting primary data on HBV reactivation rates in HBsAg-/cAb+ patients receiving rituximab. We excluded case series with less than 5 patients. Pooled estimates were calculated for HBV reactivation and the impact of HBsAb status on HBV reactivation rate was explored. We also examined reactivation in HBsAg+ patients receiving rituximab by performing a systematic review of the English language literature in PubMed (1997 to June 21, 2013) using the terms “hepatitis B virus”, “reactivation” and “lymphoma”. Results: Data from 550 HBsAg-/cAb+ patients in 12 studies were included. Using a standardized definition of HBV reactivation, (increase in HBV DNA from baseline or HBsAg seroreversion +/- ALT >3 x upper limit of normal), the pooled estimate for the risk of HBV reactivation in HBsAg-/cAb+ patients was 8.1% (I2 = 55%, P = 0.007). Significant heterogeneity was apparent. Exploratory analyses suggested that patients were less likely to reactivate if they were HBsAb+ (OR = 0.32; 95% CI 0.12-0.85, P = 0.0285). In HBsAg+ patients we meta-analyzed prospective, controlled studies. Without antiviral prophylaxis, the reactivation rate for HBsAg+ lymphoma patients was 51.0% (I2 = 0%, P = 0.93). Conclusions: Our meta-analyses confirm that there is a risk of HBV reactivation in HBsAg-/cAb+ patients exposed to rituximab. HBsAb+ patients may be at lower risk than those who are HBsAb-. However, heterogeneity in the risk estimates limits their generalizability. Without prophylaxis, significant reactivation in HBsAg+ patients exists. Large prospective studies are needed to clarify the risk of HBV reactivation in HBsAg-/cAb+ patients and to inform decisions about best practice.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.004 | 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".