Surgical Site Infection Rates Following Implementation of a Colorectal Closure Bundle in Elective Colorectal Surgeries
Bibliographic record
Abstract
BACKGROUND: Surgical site infections of up to 27% are reported for colorectal surgery. Care bundles have been introduced to decrease surgical site infection rates, but are variable in composition. OBJECTIVE: This study aimed to determine whether the addition of a "Colorectal Closure Bundle" in our Enhanced Recovery After Surgery pathway decreased surgical site infection rates. DESIGN: This is a retrospective study of elective colon resections before and after the addition of a closure bundle. SETTINGS: This study was conducted at a single academic institution. PATIENTS: Patients undergoing consecutive elective colon resections with primary anastomosis, December 2012 to July 31, 2014, enrolled in our Enhanced Recovery After Surgery pathway. Exclusion criteria were stoma creation and closure and preoperative chemoradiation. INTERVENTION: The "Colorectal Closure Bundle," which includes a change in gown and gloves, redraping, wound lavage, and a new set of instruments for closure, was added to the Enhanced Recovery After Surgery pathway. MAIN OUTCOME MEASURE: The primary outcome measured was surgical site infections as defined by CDC criteria. RESULTS: Two hundred five patients were reviewed, 111 preintervention and 94 postintervention. Overall surgical site infection rates were 25.2% preintervention vs 26.6% postintervention (p = 0.82). Surgical site infections were subdivided into "superficial" and "deep and organ space" and were 14.4% and 10.8% preintervention vs 14.9% and 11.7% postintervention (p = not significant). Smoking and diabetes mellitus were found to be independently associated with surgical site infections on multivariate analysis, with adjusted odds ratios of 4.32 (95% CI, 1.70-10.94), p = 0.002, and 2.87 (95% CI 1.30-6.34), p = 0.009. LIMITATIONS: Limitations include the retrospective nature of the study and the small sample size. CONCLUSIONS: There was no change in surgical site infection rates after implementation of the "Colorectal Closure Bundle." Smoking and diabetes mellitus were the only significant risk factors associated with increased surgical site infections. Our infection rates remain high and further change in our perioperative protocol is needed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".