A Quality-Improvement Collaborative Project to Reduce Pressure Ulcers in PICUs
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Pediatric patients are at risk for developing pressure ulcers (PUs) and associated pain, infection risk, and prolonged hospitalization. Stage III and IV ulcers are serious, reportable events. The objective of this study was to develop and implement a quality-improvement (QI) intervention to reduce PUs by 50% in our ICUs. METHODS: We established a QI collaborative leadership team, measured PU rates during an initial period of rapid-cycle tests of change, developed a QI bundle, and evaluated the PU rates after the QI implementation. The prospective study encompassed 1425 patients over 54 351 patient-days in the PICU and NICU. RESULTS: The PU rate in the PICU was 14.3/1000 patient-days during the QI development and 3.7/1000 patient-days after QI implementation (P < .05), achieving the aim of 50% reduction. The PICU rates of stages I, II, and III conventional and device-related PUs decreased after the QI intervention. The PU rate in the NICU did not change significantly over time but remained at a mean of 0.9/1000 patient-days. In the postimplementation period, 3 points were outside the control limits, primarily due to an increase in PUs associated with pulse oximeters and cannulas. CONCLUSIONS: The collaborative QI model was effective at reducing PUs in the PICU. Pediatric patients, particularly neonates, are at risk for device-related ulcers. Heightened awareness, early detection, and identification of strategies to mitigate device-related injury are necessary to further reduce PU rates.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".