Cost-effectiveness of an intraoperative antibacterial envelope in preventing cardiac implantable device-associated infections: a systematic review
Bibliographic record
Abstract
AIMS: Intraoperative use of an antibacterial envelope during surgery for cardiac implantable electronic device (CIED) surgery reduces infection risk at increased procedural costs. The objective of this systematic review was to synthesize the published economic literature on the cost-effectiveness of the antibacterial envelope. METHODS AND RESULTS: A systematic review of the published literature was conducted to identify economic evaluations (i.e. cost-utility, cost-effectiveness, and cost-benefit studies) comparing the antibacterial envelope compared with standard of care in preventing post-operative CIED infection. Systematic review best practices were followed, and study quality was assessed. Of 142 unique citations, 7 studies met the inclusion criteria for qualitative synthesis. All cost-effectiveness studies were conducted from the healthcare payer perspective of high-income countries. The base case analysis of most economic studies (5/7) reported a cost per quality-adjusted life year gained that exceeded country-specific societal thresholds for good value in healthcare. Cost-effectiveness was highly dependent on the baseline infection risk. That is, at current pricing, the antibacterial envelope may be cost-effective at base infection rates of greater than 3%, and cost-savings at infection rates that exceed 6%. CONCLUSION: Routine use of an antibacterial envelope in patients undergoing CIED procedures (implantation or revision) is unlikely to be cost-effective except among those at high risk for post-operative infection. Individualized risk assessment may help guide efficient and value-based use of this technology.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| 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) | 0.005 | 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".