Risk of Herpes Zoster in Individuals on Biologics, Disease-Modifying Antirheumatic Drugs, and/or Corticosteroids for Autoimmune Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Studies examining the risk of herpes zoster (HZ) associated with immunosuppressants, such as biologics, nonbiological disease-modifying antirheumatic drugs (nbDMARDs), or corticosteroids, have generated conflicting results. METHODS: We conducted a systematic literature search from January 1946 to February 2016. Search terms related to HZ, rheumatoid arthritis, psoriasis, psoriatic arthritis, systemic lupus erythematous, or inflammatory bowel disease, biologics, nbDMARDS, and corticosteroids were used. We included randomized controlled trials (RCTs) and observational studies reporting associations between immunosuppressants and HZ outcomes in adults. For RCTs, we used the Mantel-Haenszel fixed-effects model to estimate pooled odds ratios (ORs) and 95% confidence intervals (CIs) for HZ risk. For observational studies, adjusted ORs were pooled separately using random-effects inverse variance models. RESULTS: Data were pooled from 40 eligible RCTs (20136 patients) and 19 observational studies (810939 patients). Biologics were associated with a greater risk of HZ than control (RCTs: OR = 1.71, 95% CI = 1.11-2.64; observational studies: OR = 1.58, 95% CI = 1.39-1.81). In RCTs, the OR of non-tumor necrosis factor (TNF) blockers was 2.19 (95% CI 1.20-4.02), but that of TNF blockers was not significantly different from control. Increased risks of HZ with nbDMARDs (OR = 1.21; 95% CI = 1.15-1.28) and corticosteroids (OR = 1.73; 95% CI = 1.57-1.89) were observed in observational studies, but few RCTs examined these comparisons. CONCLUSIONS: Immunocompromised patients receiving biologics were associated with an increased risk of HZ. The risk is also increased with corticosteroids and nbDMARDs. These findings raise the issue of prophylaxis with zoster vaccine in patients initiating immunosuppressive therapy for autoimmune diseases.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.009 | 0.010 |
| 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".