Risk of Postoperative Infectious Complications From Medical Therapies in Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of inflammatory bowel disease (IBD) medications on postoperative infection risk within 30 days of surgery. METHODS: We searched multiple electronic databases and reference lists of articles dating up to August 2018 for prospective and retrospective studies comparing postoperative infection risk in patients treated with an IBD medication perioperatively with the risk in patients who were not taking that medication. Outcomes were overall infectious complications and intra-abdominal infections within 30 days of surgery. RESULTS: Sixty-three studies were included. Overall infectious complications were increased in patients who received anti-tumor necrosis factor (TNF) agents (odds ratio [OR] 1.26; 95% confidence interval [CI], 1.07-1.50) and corticosteroids (OR 1.34; 95% CI, 1.25-1.44) and decreased in those who received 5-aminosalicylic acid (OR 0.63; 95% CI, 0.46-0.87). No difference was observed in those treated with immunomodulators (OR 1.08; 95% CI, 0.94-1.25) or anti-integrin agents (OR 1.06; 95% CI, 0.67-1.69). Both corticosteroids and anti-TNF agents were associated with increased intra-abdominal infection risk (OR 1.63; 95% CI, 1.33-2.00 and OR 1.46; 95% CI, 1.08-1.97, respectively), whereas no impact was observed with 5-aminosalicylates, immunomodulators, or anti-integrin therapy. Twenty-two studies had low risk of bias while the remaining studies had very high risk. CONCLUSIONS: Corticosteroids and anti-TNF agents were associated with increased overall postoperative infection risk as well as intra-abdominal infection in IBD patients, whereas no increased risk was observed for immunomodulators or anti-integrin therapy. Although these results may result from residual confounding rather than from a true biological effect, prospective studies that control for potential confounding factors are required to generate higher-quality evidence.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".