Impact of a Protocol for Prevention of Ventilator-Associated Pneumonia
Bibliographic record
Abstract
BACKGROUND: Several interventions have been shown to be effective in reducing the incidence of ventilator-associated pneumonia (VAP), but their implementation in clinical practice has not gained widespread acceptance. OBJECTIVE: To determine the impact of a protocol that incorporates evidence-based interventions shown to reduce the frequency of VAP on the overall rate of VAP, early-onset VAP, and late-onset VAP in the intensive care unit (ICU) of a tertiary care adult teaching hospital. METHODS: This pre- and postintervention observational study included mechanically ventilated patients admitted to the Montreal General Hospital ICU between November 2003 and May 2004 (preintervention) and between November 2004 and May 2005 (postintervention). A multidisciplinary prevention protocol was developed, implemented, and reinforced. Rates of VAP per 1000 ventilator-days were calculated pre- and postprotocol implementation for all patients, for patients with early-onset VAP, and for those with late-onset VAP. RESULTS: In the pre- and postintervention groups, 349 and 360 patients, respectively, were mechanically ventilated. Twenty-three VAP episodes occurred in 925 ventilator-days (crude incidence rate 25 per 1000) in the preintervention period. Following implementation, the VAP rate decreased to 22 episodes in 988 ventilator-days (crude incidence rate 22.3 per 1000), corresponding to a relative reduction in rate of 10.8% (p < 0.001). The incidence of early-onset VAP decreased from 31.0 to 18.5 VAP per 1000 ventilator-days (p < 0.001), while the incidence of late-onset VAP increased from 21.9 to 24.1 VAP per 1000 ventilator-days (p < 0.001). However, when all covariates were adjusted, the impact of the prevention protocol was not statistically significant. CONCLUSIONS: Implementation of a VAP prevention protocol incorporating evidence-based interventions reduced the crude incidence of VAP, early-onset VAP, and late-onset VAP. However, when covariates were adjusted, the beneficial effect was no longer observed. Further research is needed to assess the impact of such measures on VAP, early-onset VAP, and late-onset VAP.
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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 | 0.072 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".