Patient engagement to counter catheter-associated urinary tract infections with an app (PECCA): a multicentre, prospective, interrupted time-series and before-and-after study
Bibliographic record
Abstract
BACKGROUND: The risk of urinary tract infections (UTIs) is increased by unnecessary placement and prolonged use of urinary catheters. AIM: To assess whether inappropriate use of catheters and catheter-associated UTI were reduced through patient participation. METHODS: In this multicentre, interrupted time-series and before-and-after study, we implemented a patient-centred app which provides catheter advice for patients, together with clinical lessons, feedback via e-mails and support rounds for staff members. Data on catheter use and infections were collected during a six-month baseline and a six-month intervention period on 13 wards in four hospitals in the Netherlands. Dutch Trial Register: NL7178. FINDINGS: , 2019, 6556 patients were included in 24 point-prevalence surveys, 3285 (50%) at baseline and 3271 (50%) during the intervention. During the intervention 249 app users and a median of seven new app users per week were registered (interquartile range: 5.5-13.0). At baseline, inappropriate catheter use was registered for 175 (21.9%) out of 798 catheters, compared to 55 (7.0%) out of 786 during the intervention. Time-series analysis showed a non-significant decrease of inappropriate use of 5.8% (95% confidence interval: -3.76 to 15.45; P = 0.219), with an odds ratio of 0.27 (0.19-0.37; P < 0.001). Catheter-associated UTI decreased by 3.0% (1.3-4.6; P = 0.001), with odds ratio 0.541 (0.408-0.716; P < 0.001). CONCLUSION: Although UTI significantly decreased after the implementation, patient participation did not significantly reduce the prevalence of inappropriate urinary catheter use. However, the inappropriate catheter reduction of 5.8% and an odds ratio of 0.27 suggest a positive trend. Patient participation appears to reduce CAUTI and could reduce other healthcare-associated infections.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".