Sternal-Wound Infections following Coronary Artery Bypass Graft: Could Implementing Value-Based Purchasing be Beneficial?
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Sternal-wound infections (SWIs) are rare but consequential healthcare-healthcare-associated infections following coronary artery bypass graft surgery (CABG). The impact of SWIs associated on the cost of health care provision is unknown. The aim of this study was to quantify the burden of CABG-related SWIs across countries with mature health care systems and estimate value-based purchasing (VBP) levels based on the local burden. METHODS: A structured literature review identified relevant data for 14 countries (the Netherlands, France, Germany, Austria, the United Kingdom, Canada, Italy, Japan, Spain, the United States, Brazil, Israel, Taiwan, and Thailand). Data, including SWI rates, CABG volume, and length of stay, were used to populate a previously published Markov model that simulates the patient's CABG-care pathway and estimates the economic (US$) and care burden of SWIs for each country. Based on this burden, scenarios for VBP were explored for each country. A feasible cost of intervention per patient for an intervention providing a 20% reduction in the SWI rate was calculated. RESULTS: The SWI burden varied considerably between settings, with SWIs occurring in 2.8% (the United Kingdom) to 10.4% (the Netherlands) of CABG procedures, while the costs per SWI varied between US$8172 (Brazil) to US$54 180 (Japan). Additional length of stay after SWI was the largest cost driver. The overall highest annual burden was identified in the United States (US$336 million) at a mean cost of US$36 769 per SWI. Given the SWI burden, the median cost of intervention per patient that a hospital could afford ranged from US$20 (US$13 to US$42) in France to US$111 (US$65 to US$183) in Japan. CONCLUSIONS: SWIs represent a large burden with a median cost of US$13 995 per case and US$900 per CABG procedure. By tackling SWIs, there is potential to simultaneously reduce the burden on health care systems and improve outcomes for patients. Mutually beneficial VBP agreements might be one method to promote uptake of novel methods of SWI prevention.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".