Reduction in surgical site infections in the Southern Cross Hospitals network, 2004-2015: successful outcome of a long-term surveillance and quality improvement project.
Bibliographic record
Abstract
AIM: To report on the reduction in the surgical site infection (SSI) rate in the Southern Cross Hospitals network over a 12-year period, 2004-2015, following active surveillance and quality improvement actions. METHODS: Ten hospitals in the network performed prospective SSI surveillance using standard definitions across a range of ten surgical procedure groups. Data was manually collected on a standardised form and entered into a bespoke database. Information collected included timing and dose of surgical antibiotic prophylaxis, type of surgical site skin preparation used, and patient information on smoking, diabetes and body mass index (BMI). Patients were contacted 30 days after their elective surgery to detect SSIs presenting after discharge from hospital. Surveillance results were widely reported to infection control and clinical review committees. Quality improvement activities to increase use of best practice interventions for surgical antibiotic prophylaxis and alcohol-based skin preparations were initiated during the surveillance period. RESULTS: 42,792 procedures performed in ten hospitals were analysed. There were 932 (2.2%) SSIs. The SSI rate decreased from 3.5% in 2004 to 1.2% in 2015, r-0.865, p=<0.0001, a decrease of 59%, approximately 5% a year. Rates decreased in seven of the 10 hospitals, p≤0.02 for each, and in five of the ten procedure groups, p≤0.02 for each. Diabetic patients, odds ratio (OR) 1.4 (95% confidence interval (CI) 1.1-1.9), obese patients (BMI>30), OR 2.0 (95% CI 1.6-2.4), and those with a surgical risk score of ≥1 OR 1.3 (95% CI 1.1-1.6) had higher SSI rates. These three patient risk factors increased during the 12-year period. The use of alcohol-based skin preparations increased during the period from 63% to 84% in the first two and last two years respectively, p<0.0001. Use of an alcohol-based skin preparation was associated with a reduction in SSIs OR 0.54 (95% CI 0.47-0.62). On time prophylaxis improved from 72% to 95% over the 12 years, p<0.0001, and on time prophylaxis was associated with a reduction in SSIs, OR=0.62 (95% CI 0.51-0.75). The use of 2g doses of cefazolin increased significantly after 2010, p<0.0001. The most common cause of SSI was Staphylococcus aureus which was present in 54% of cases with a positive culture. CONCLUSIONS: This long-term surveillance and quality improvement programme has made a significant contribution to the overall reduced rate of SSIs in Southern Cross Hospitals. This reduction occurred despite patient risk factors for SSI increasing. Further reduction is possible with higher adherence to best practice and interventions aimed at reducing S. aureus SSIs.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".