Management of perioperative tumour necrosis factor α inhibitors in rheumatoid arthritis patients undergoing arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Tumour necrosis factor α inhibitors (TNFis) are widely used in RA patients who undergo surgery, and optimal perioperative management must balance the risk of infection with the risk of post-operative flare. The purpose of this study is to examine the impact of TNFi exposure on surgical site infections (SSIs) in RA patients undergoing elective orthopaedic surgery by systematic review and meta-analysis. METHODS: A systematic review of the literature and meta-analysis were performed using PUBMED, EMBASE and the Cochrane Central Register of Controlled Trials, through May 2014. Two independent reviewers screened titles and abstracts, and analysed selected papers in detail. Included studies assessed RA patients with or without TNFi exposure prior to orthopaedic surgery, and described post-operative infections. Study quality was assessed using the Oxford Centre for Evidence-based Medicine Levels of Evidence. Meta-analyses of the individual study odds ratios (ORs) were conducted, and each pooled OR was calculated using a random effects model. RESULTS: Eight observational studies and three case control studies met inclusion criteria; risk of bias was low in eight studies and moderate in three. Publication bias was not apparent. These studies represent 3681 patients with recent exposure to TNFis (TNFi+) and 4310 with no recent exposure to TNFis (TNFi-) at the time of surgery. The TNFi+ group had higher risk of developing SSI compared with patients in the TNFi- group (random effects model: OR 2.47 (95% CI 1.66, 3.68); P < 0.0001). CONCLUSION: Data from the available literature suggest that there is an increased risk of SSIs in RA patients who use or have recently used TNFis at the time of elective orthopaedic surgery. Prospective studies to confirm these findings and establish the optimal withhold and restart time of TNFis, in the context of other risk factors for infection in RA patients such as higher disease activity, corticosteroid use, smoking and diabetes, are needed.
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 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| 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".