Bacteriology of endotracheal tube biofilms and antibiotic resistance: a systematic review
Bibliographic record
Abstract
Bacteria commonly adhere to surfaces and produce polymeric material to encase the attached cells to form communities called biofilms. Within these biofilms, bacteria can appear to be many times more resistant to antibiotics or disinfectants. This systematic review explores the prevalence and microbial profile associated with biofilm production of bacteria isolated from endotracheal tubes and its associations with antimicrobial resistance. A comprehensive search was performed on databases PubMed, Embase, and Google Scholar for relevant articles published between 1st January 2000 and 31st December 2022. The relevant articles were exported to Mendeley Desktop 1.19.8 and screened by title and abstract, followed by full text screening based on the eligibility criteria of the study. Quality assessment of the studies was performed using the Newcastle–Ottawa Scale (NOS) customized for cross-sectional studies. Furthermore, the prevalence of antimicrobial resistance in biofilm-producers isolated from endotracheal tube specimens was investigated. Twenty studies encompassing 981 endotracheal tubes met the eligibility criteria. Pseudomonas spp. and Acinetobacter spp. were predominant isolates among the biofilm producers. These biofilms provided strong resistance against commonly used antibiotics. The highest resistance rate observed in Pseudomonas spp. was against fluoroquinolones whereas the least resistance was seen against piperacillin-tazobactam. A similar trend of susceptibility was observed in Acinetobacter spp. with a very high resistance rate against fluoroquinolones, third-generation cephalosporins and carbapenems. In conclusion, endotracheal tubes were associated with colonization by biofilm forming bacteria with varying levels of antimicrobial resistance. Biofilms may promote the occurrence of recalcitrant infections in endotracheal tubes which need to be managed with appropriate protocols and antimicrobial stewardship. Research focus should shift towards meticulous exploration of biofilm-associated infections to improve detection and management.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".