Causative Agents of Ventilator-Associated Pneumonia and Resistance to Antibiotics in COVID-19 Patients: A Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
Patients with coronavirus disease 2019 (COVID-19) have an increased risk of ventilator-associated pneumonia (VAP). This systematic review updates information on the causative agents of VAP and resistance to antibiotics in COVID-19 patients. We searched the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed/MEDLINE, and LILACS databases from December 2019 to December 2021. Studies that described the frequency of causative pathogens associated with VAP and their antibiotic resistance patterns in critically ill COVID-19 adult patients were included. The Newcastle-Ottawa Quality Assessment Scale was used for critical appraisal. The data are presented according to the number or proportions reported in the studies. A total of 25 articles were included, involving 2766 VAP cases in COVID-19 patients (range 5–550 VAP cases). Most of the studies included were carried out in France (32%), Italy (20%), Spain (12%) and the United States (8%). Gram-negative bacteria were the most frequent causative pathogens of VAP (range of incidences in studies: P. aeruginosa 7.5–72.5%, K. pneumoniae 6.9–43.7%, E. cloacae 1.6–20% and A. baumannii 1.2–20%). S. aureus was the most frequent Gram-positive pathogen, with a range of incidence of 3.3–57.9%. The median incidence of Aspergillus spp. was 6.4%. Few studies have recorded susceptibility patterns among Gram-negative causative pathogens and have mainly reported extended-spectrum beta-lactamase (ESBL), AmpC, and carbapenem resistance. The median frequency of methicillin resistance among S. aureus isolates was 44.4%. Our study provides the first comprehensive description of the causative agents and antibiotic resistance in COVID-19 patients with VAP. Gram-negative bacteria were the most common pathogens causing VAP. Data on antibiotic resistance patterns in the published medical literature are limited, as well as information about VAP from low- and middle-income countries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it