Tigecycline-Containing Regimens and Multi Drug-Resistant <i>Acinetobacter baumannii</i> : A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: The use of tigecycline (TG) for the treatment of Acinetobacter baumannii is controversial. In this systematic review and meta-analysis, we aimed to better explore the safety and efficacy of TG for the treatment of multi drug-resistant (MDR) Acinetobacter. Methods: We searched PubMed/MEDLINE, Scopus, Cochrane Central, and Web of Science to identify studies reporting the clinical and microbiological efficacy and safety of regimens containing TG in patients with drug susceptibility testing (DST)-confirmed MDR A. baumannii , published until December 30, 2022. Observational studies were included if they reported clinical and microbiological efficacy of TG-based regimens. The Newcastle-Ottawa Scale (NOS) and Joana Briggs Institute (JBI) critical appraisal tool were used to assess the quality of included studies. Results: There were 30 observational studies, of which 19 studies were cohort and 11 studies were single group studies. Pooled clinical response and failure rates in the TG-containing regimens group were 58.1 (95% confidence interval [CI] 49.2–66.6) and 40.2 (95% CI 31.1–50.0), respectively. The pooled microbiological response rate was 32.1 (95% CI 19.8–47.5), and the pooled all-cause mortality rate was 41.1 (95% CI 34.1–48.4). Pooled clinical response and failure rates in the colistin-based regimens group were 52.7 (42.7–62.5) and 43.1 (33.1–53.8), respectively. The pooled microbiological response rate was 42.9 (16.2–74.5), and the pooled all-cause mortality rate was 34.3 (26.1–43.5). Conclusions: According to our results, the efficacy of the TG-based regimen is the same as other antibiotics. However, our study showed a high mortality rate and a lower rate of microbiological eradication for TG compared with colistin-based regimen. Therefore, our study does not recommend it for the treatment of MDR A. baumannii . However, this was a prevalence meta-analysis of observational studies, and for better conclusion experimental studies are required.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.009 | 0.010 |
| 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".