Antimicrobial resistance among agents of hospital-acquired lower respiratory tract infection in the UK and Ireland: trends from 2008/2009 to 2018/2019
Bibliographic record
Abstract
OBJECTIVES: To survey trends in antimicrobial resistance among the pathogens of hospital-acquired lower respiratory tract infection (HA-LRTI), which causes significant mortality and morbidity, particularly among ventilated patients. METHODS: The BSAC Surveillance collected quotas of major HA-LRTI pathogens from sentinel sites from 2008/09 (October to September) to 2018/19. MIC testing was by BSAC agar dilution. Resistance mechanisms were inferred from synergy tests, interpretive reading and PCR. RESULTS: Target numbers of Staphylococcus aureus, Pseudomonas aeruginosa and Enterobacterales-dominated by Escherichia coli and Klebsiella spp.-were reliably collected. Acinetobacter spp. collections were small, reflecting low incidence. Resistance rates fell or fluctuated, with no major rises. Notable declines included: (i) a fall in the proportion of MRSA among S. aureus from c. 40% to 10%; (ii) a halving, since 2012/13, in 'triple-resistance' to carbapenems, aminoglycosides and fluoroquinolones among Acinetobacter baumannii sensu stricto, from c. 24% to 9%; (iii) reductions in AmpC-associated cephalosporin resistance among Enterobacter cloacae and Serratia isolates, and (iv) falls in fluoroquinolone resistance among Enterobacterales, except Klebsiella pneumoniae. Resistance rates in P. aeruginosa remained low, though higher than in bacteraemia. Cephalosporin resistance in E. coli and K. pneumoniae was largely ESBL associated and, unlike AmpC-associated resistance in Enterobacter and Serratia spp., did not decline notably. Except for OXA-23 in A. baumannii, carbapenemases remained extremely rare. Antistaphylococcal oxazolidinones, tigecycline, ceftolozane/tazobactam, ceftazidime/avibactam and ceftobiprole retained uneroded activity. CONCLUSIONS: From 2008/09 to 2018/19, there were no major rises in resistance among the principal agents of HA-LRTI; for several important organisms/resistance combinations there were notable declines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".