The effectiveness of a Comprehensive Device Associated Healthcare-Associated Infections Prevention and Control Program: results of a 3-year program in the Republic of Cyprus
Bibliographic record
Abstract
Abstract Background: Device-associated health care-associated infections (DA-HAIs) are a major threat to patient safety, particularly in the Intensive Care Unit (ICU). The aim of this study was to evaluate the effectiveness of a bundle of infection control measures to reduce DA-HAIs in the ICU of a General Hospital in the Republic of Cyprus, over a three-year period. Methods: We studied 599 ICU patients with length of stay (LOS) for at least 48 hours. Our prospective cohort study was divided into three surveillance phases. VAP, CLABSI, and CAUTI incidence rates, LOS and mortality were calculated before, during and after the infection prevention and control program. Results: There was a statistically significant reduction in the number of DA-HAI events during the surveillance periods, associated with DA-HAIs prevention efforts. In 2015 (prior to program implementation), the baseline DA-HAIs instances were 43: 16 VAP (10.1/1000 Device Days), 21 (15.9/1000DD) CLABSIs and 6 (2.66/1000DD) CAUTIs, (n=198). During the second phase (2016), CLABSIs prevention measures were implemented and the number of infections were 24: 14 VAP (12.21/1000DD), 4 (4.2/1000DD) CLABSIs & 6 (3.22/1000DD) CAUTIs, (n=184). During the third phase (2017), VAP and CAUTI prevention measure were again implemented and the rates were 6: (3 VAP: 12.21/1000DD), 2 (1.95/1000DD) CLABSIs & 1 (0.41/1000DD) CAUTIs, (n=217). There was an overall reduction of 87% in the total number of DA-HAIs instances for the period 01/01/15 to 31/12/17. Conclusions: The significant overall reduction in DA-HAI rates, indicates that a comprehensive infection control program can affect DA-HAI 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, 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".