Risk factors for catheter‐associated urinary tract infection among hospitalized patients: A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
AIMS: The study aimed to identify the risk factors for catheter-associated urinary tract infection among hospitalized patients. We also tried to explore its potential effect on patient outcomes if possible. BACKGROUND: Catheter-associated urinary tract infection accounts for a large proportion of healthcare-associated infections and remains a considerable threat to patient safety worldwide. DESIGN: A systematic review and meta-analysis of observational studies. DATA SOURCES: We conducted an electronic search in PubMed, EMBASE, Web of Science, and the Cochrane Database of Systematic Reviews for studies published between January 2008-January 2018. REVIEW METHODS: Two reviewers searched the articles and extracted the data independently. The quality of the studies was assessed with the Newcastle-Ottawa Scale. RevMan 5.3 was used to perform the meta-analysis. RESULTS: Ten studies involving a total of 8785 participants with or without catheter-associated urinary tract infection were included. The average incidence of catheter-associated urinary tract infection was 13.79 per 1000 catheter days, with a prevalence rate of 9.33%. The meta-analysis demonstrated that patients at high risk for catheter-associated urinary tract infection were female, had a prolonged duration of catheterization, had diabetes, had previous catheterization, and had longer hospital and ICU stays. Additionally, catheter-associated urinary tract infection was also accompanied by an increase in mortality. CONCLUSIONS: Healthcare staff should focus on the identified risk factors for catheter-associated urinary tract infection. Further research is needed to investigate the microbial isolates and focus on the intervention strategies of catheter-associated urinary tract infection, so as to reduce its incidence and related 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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".