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 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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".