Surveillance of device associated infections and mortality in a major intensive care unit in the Republic of Cyprus
Bibliographic record
Abstract
BACKGROUND: Device-associated health care-associated infections (DA-HAI) pose a threat to patient safety, particularly in the intensive care unit. The aim of this study was to assess the incidence of DA-HAIs, mortality and crude excess mortality at a General Hospital's Intensive Care Unit (ICU) in the Republic of Cyprus for 1 year period. METHODS: A prospective cohort, active DA-HAIs surveillance study with the use of Health Acquired Infections (HAIs) ICU Protocol (v1.01 standard edition) as provided by ECDC/NHSN for the active DA-HAIs surveillance study was conducted. The study sample included 198 ICU patients admitted during the research period and hospitalized for over 48 h. The Ventilator-Associated Pneumonia (VAP), Central Line-Associated Bloodstream Infection (CLABSI), and Catheter-Associated Urinary Tract Infection (CAUTI) rates, length of stay (LOS), mortality, and crude excess mortality were calculated. RESULTS: CLABSI was the most frequent DA-HAI with 15.9 incidence rate per 1000 Central Venus Catheter (CVC) days. The VAP rate, was 10.1 per 1000 ventilator days and the CAUTI rate was 2.7 per 1000 urinary catheter days. Device associated infections were found to be significantly associated with the length of ICU stay (p < 0.001), the CVC days (p < 0.001), ventilator days (p < 0.001), and urinary catheter days (p < 0.001). The excess mortality was 22.1% for those who acquired a DA-HAI (95% CI, 2-42.2%) compared to the patients who remained DA-HAI free. Mortality of patients with VAP infection was 2.3 times higher (RR = 2.33 95% CI, 1.07-5.05) than those patients admitted without a HAI and subsequently did not acquire a DA-HAI. The most frequently isolated pathogen was Staphylococcus epidermidis (13.9%) and Candida albicans (13.9%). CONCLUSIONS: Higher DA-HAIs rates and device utilization than the international benchmarks were found in this study, calling into question the safety of preventative practices employed in this unit.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".