Chlorhexidine-Related Mortality Rate in Critically Ill Subjects in Intensive Care Units: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: This meta-analysis aimed to explore the chlorhexidine-related mortality rate for subjects on mechanical ventilation and in an ICU when compared with subjects who received standard ICU care. METHODS: We searched a number of medical literature databases and the first 100 results in an internet search. Two of us independently reviewed the titles and abstracts of the identified articles. Then general and specific characteristics from eligible articles were extracted and the quality of included trials were appraised by using a risk of bias assessment tool. Risk ratios were calculated, together with the 95% CI. Random-effects models with the Mantel-Haenszel method were used to estimate pooled probabilities. Heterogeneity was identified and quantified via the chi square test and I 2 values, respectively. RESULTS: Eleven of the 547 studies were suitable for this meta-analysis. The included participants were critically ill adults in ICU settings of high-income countries ( n = 1157) and low/ middle-income countries ( n = 612). They were assigned to either the chlorhexidine or control groups. Overall, moderate-quality evidence indicated reduced ventilator-associated pneumonia incidence (for high-income countries: RR 0.60, 95% CI 0.41–0.87; P = .008; I 2 = 39%; and for low- and middle-income countries: RR 0.71, 95% CI 0.51–0.99; P = .05; I 2 = 10%), without a substantial effect on mortality rate (for high-income countries: RR 1.01, 95% CI 0.65–1.57; P = .96; I 2 = 42%; and for low- and middle-income countries: RR 1.11, 95% CI 0.96–1.29; P = .17; I 2 = 0%). CONCLUSIONS: The prophylactic administration of chlorhexidine among patients who were critically ill and in an ICU setting reduced the occurrence of ventilator-associated pneumonia with no significant impact on associated mortality.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.061 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".