Risk factors associated with bloodstream infections caused by Acinetobacter baumannii in hospital settings: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Acinetobacter baumannii represents a significant public health issue in hospital settings because of its high-level resistance to various antimicrobials. Early detection of patients at risk of A. baumannii bloodstream infection (BSI) is crucial. Objectives To investigate risk factors for A. baumannii BSI by means of a systematic review and meta-analysis. Methods MEDLINE, Embase, Web of Science and Scopus libraries were used to systematically search available evidence. Two authors independently performed screening, full-text review, data extraction and quality assessments. A third author resolved any conflicts. A meta-analysis was conducted with common and random effects models for risk factors investigated in at least four studies. Results Overall, 25 studies (13 cohort and 12 case-control studies) were included. All studies were classified as good quality studies using the Newcastle-Ottawa scale (≥ 6/9 stars). Most studies were performed in intensive care units on critically ill patients. Only one study used simple logistic regression, while the majority performed multivariate regression. The meta-analysis revealed that previous antimicrobial therapy (OR 2.30, 95 % CI 1.89 - 2.81), central-line catheter use (OR 3.51, 95 % CI 2.45 – 5.01) and mechanical ventilation (OR 3.29, 95 % CI 2.13 - 5.09) were associated with an increased risk of developing A. baumannii BSI. Conclusion Previous antimicrobial therapy, central-line catheter use, and mechanical ventilation were associated with A. baumannii bloodstream infections. Infection prevention and control programmes should address these risk factors to reduce such infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".