Efficacy of antimicrobial envelopes in preventing cardiac implantable electronic device infection – systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cardiac implantable electronic device (CIED) infections remain a significant complication of increasingly common CIED procedures. This systematic review aims to evaluate the efficacy of antibiotic eluting envelopes (AEEs) in preventing CIED infections. METHODS: A systematic search of MEDLINE, CINAHL, Embase, Scopus, and Cochrane Library was conducted from inception to September 2024. Human studies evaluating TYRX AEE or CanGaroo envelopes hydrated in antibiotics were eligible. Studies on CanGaroo envelope were excluded from meta-analysis. RoB 2 and ROBINS-I tools were used to assess risk of bias. RESULTS: Fourteen studies and 87,184 patients were included. AEE use did not result in statistically significant reduction in odds of any CIED infection over entire study duration (odds ratio [OR], 0.73; 95% confidence interval [CI], 0.49-1.08) or within 12 months (OR, 0.85; 95% CI, 0.62-1.18), major CIED infection over entire study duration (OR, 0.73; 95% CI, 0.44-1.22) or within 12 months (OR, 0.79; 95% CI, 0.46-1.37), minor CIED infection (OR, 0.75; 95% CI, 0.48-1.18) or mortality (OR, 1.07; 95% CI, 0.60-1.88). However, in high infection risk patients, AEE use was associated with reduction in any infections over entire study duration (OR, 0.66; 95% CI, 0.45-0.97) and within 12 months (OR, 0.73; 95% CI, 0.56-0.95). CONCLUSIONS: Limitations of this review include group difference and scarcity of data regarding comorbidities in some studiesk. Overall, AEE use is not effective in reducing odds of CIED infection, although it may benefit high infection risk patients. TRIAL REGISTRATION: International Register of Prospective Systematic Reviews (PROSPERO) Identifier: CRD42024588950.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| 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".