The global prevalence of Daptomycin, Tigecycline, Quinupristin/Dalfopristin, and Linezolid-resistant Staphylococcus aureus and coagulase–negative staphylococci strains: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-resistant coagulase-negative Staphylococcus (MRCoNS) are among the main causes of nosocomial infections, which have caused major problems in recent years due to continuously increasing spread of various antibiotic resistance features. Apparently, vancomycin is still an effective antibiotic for treatment of infections caused by these bacteria but in recent years, additional resistance phenotypes have led to the accelerated introduction of newer agents such as linezolid, tigecycline, daptomycin, and quinupristin/dalfopristin (Q/D). Due to limited data availability on the global rate of resistance to these antibiotics, in the present study, the resistance rates of S. aureus, Methicillin-resistant S. aureus (MRSA), and CoNS to these antibiotics were collected. METHOD: Several databases including web of science, EMBASE, and Medline (via PubMed), were searched (September 2018) to identify those studies that address MRSA, and CONS resistance to linezolid, tigecycline, daptomycin, and Q/D around the world. RESULT: Most studies that reported resistant staphylococci were from the United States, Canada, and the European continent, while African and Asian countries reported the least resistance to these antibiotics. Our results showed that linezolid had the best inhibitory effect on S. aureus. Although resistances to this antibiotic have been reported from different countries, however, due to the high volume of the samples and the low number of resistance, in terms of statistical analyzes, the resistance to this antibiotic is zero. Moreover, linezolid, daptomycin and tigecycline effectively (99.9%) inhibit MRSA. Studies have shown that CoNS with 0.3% show the lowest resistance to linezolid and daptomycin, while analyzes introduced tigecycline with 1.6% resistance as the least effective antibiotic for these bacteria. Finally, MRSA and CoNS had a greater resistance to Q/D with 0.7 and 0.6%, respectively and due to its significant side effects and drug-drug interactions; it appears that its use is subject to limitations. CONCLUSION: The present study shows that resistance to new agents is low in staphylococci and these antibiotics can still be used for treatment of staphylococcal infections in the world.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".