Risk of Herpes Zoster in Immunocompromised Individuals on Conventional DMARDS, Biologics or Corticosteroids: A Meta-Analysis
Bibliographic record
Abstract
Background. Patients with impaired T-cell immunity, like those on immunosuppressants (IMS) for autoimmune diseases, are at higher risk of herpes zoster (HZ). Studies looking at the relative risk of HZ associated with corticosteroids, biologics or conventional disease modifying agents (cDMARD) have generated conflicting results. This study aimed to determine the association of these agents with the risk of HZ in patients with autoimmune diseases through a meta-analysis. Methods. A systematic search with MEDLINE, EMBASE, Google, Google Scholar, CENTRAL, CAB Direct, CINAHL, Web of Knowledge, and PubMed for studies published in English (January 1946–February 2015) and reporting HZ outcomes in adults with autoimmune diseases on IMS was conducted. Search terms related to HZ, autoimmune diseases and IMS were used. For randomized controlled trials (RCT), due to rare events, the Mantel-Haenszel method with continuity correction was performed to estimate the pooled odds ratio (OR) and 95% confidence interval [CI] for HZ associated with various IMS. For observational studies (OBS), adjusted ORs were pooled separately using the inverse variance method. Results. Out of the 3756 identified studies, 44 were included (n = RCT: 47,152, OBS: 3,117,387). Biologics were associated with a greater risk of HZ than control (RCT: OR 1.69, 95% CI 1.09-2.62; OBS: 1.48, 95% CI 1.31-1.67). In RCT, the OR of TNF blockers was 2.10 (95% CI 1.17-3.77) but that of non-TNF blockers was not significantly different from control (i.e. placebo/ non-use). A small increased risk of HZ with cDMARD (OR 1.20, 95% CI 1.15-1.25) was observed in OBS but not in RCT; and the risk was lower compared to biologics (OBS: OR 1.23, 95% CI 1.03-1.48, not significant in RCT). Combinations with biologics was associated a greater risk of HZ (OBS: OR 2.47, 95% CI 1.91-3.19). HZ risk was also increased with corticosteroid use alone versus non-use (OBS: OR 1.78, 95% CI 1.72-1.84). Conclusion. This study showed an increased risk of HZ in patients receiving biologics, especially TNF blockers. The trend was also observed with corticosteroids, less so for cDMARD and higher with combinations involving biologics. These findings raise the issue of prophylaxis with zoster vaccines in patients initiating IMS for autoimmune diseases. Disclosures. F. Marra, Merck Canada Inc: Grant Investigator, Research grant.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.074 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".