Standardisation of perioperative urinary catheter use to reduce postsurgical urinary tract infection: an interrupted time series study
Bibliographic record
Abstract
BACKGROUND: Prevention of healthcare-associated urinary tract infection (UTI) has been the focus of a national effort, yet appropriate indications for insertion and removal of urinary catheters (UC) among surgical patients remain poorly defined. METHODS: We developed and implemented a standardised approach to perioperative UC use to reduce postsurgical UTI including standard criteria for catheter insertion, training of staff to insert UC using sterile technique and standardised removal in the operating room and surgical unit using a nurse-initiated medical directive. We performed an interrupted time series analysis up to 2 years following intervention. The primary outcome was the proportion of patients who developed postsurgical UTI within 30 days as measured by the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Process measures included monthly UC insertions, removals in the operating room and UC days per patient-days on surgical units. RESULTS: At baseline, 22.5% of patients were catheterised for surgery, none were removed in the operating room and catheter-days per patient-days were 17.4% on surgical units. Following implementation of intervention, monthly catheter removal in the operating room immediately increased (range 12.2%-30.0%) while monthly UC insertion decreased more slowly before being sustained below baseline for 12 months (range 8.4%-15.6%). Monthly catheter-days per patient-days decreased to 8.3% immediately following intervention with a sustained shift below the mean in the final 8 months. Postsurgical UTI decreased from 2.5% (95% CI 2.0-3.1%) to 1.4% (95% CI 1.1-1.9; p=0.002) during the intervention period. CONCLUSIONS: Standardised perioperative UC practices resulted in measurable improvement in postsurgical UTI. These appropriateness criteria for perioperative UC use among a broad range of surgical services could inform best practices for hospitals participating in ACS NSQIP.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".