Carbapenemase-producing Enterobacteriaceae in the UK: a national study (EuSCAPE-UK) on prevalence, incidence, laboratory detection methods and infection control measures
Bibliographic record
Abstract
OBJECTIVES: To estimate UK prevalence and incidence of clinically significant carbapenemase-producing Enterobacteriaceae (CPE), and to determine epidemiological characteristics, laboratory methods and infection prevention and control (IPC) measures in acute care facilities. METHODS: A 6 month survey was undertaken in November 2013-April 2014 in 21 sentinel UK laboratories as part of the European Survey on Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) project. Up to 10 consecutive, non-duplicate, clinically significant and carbapenem-non-susceptible isolates of Escherichia coli or Klebsiella pneumoniae were submitted to a reference laboratory. Participants answered a questionnaire on relevant laboratory methods and IPC measures. RESULTS: Of 102 isolates submitted, 89 (87%) were non-susceptible to ≥1 carbapenem, and 32 (36%) were confirmed as CPE. CPE were resistant to most antibiotics, except colistin (94% susceptible), gentamicin (63%), tigecycline (56%) and amikacin (53%). The prevalence of CPE was 0.02% (95% CI = 0.01%-0.03%). The incidence of CPE was 0.007 per 1000 patient-days (95% CI = 0.005-0.010), with north-west England the most affected region at 0.033 per 1000 patient-days (95% CI = 0.012-0.072). Recommended IPC measures were not universally followed, notably screening high-risk patients on admission (applied by 86%), using a CPE 'flag' on patients' records (70%) and alerting neighbouring hospitals when transferring affected patients (only 30%). Most sites (86%) had a laboratory protocol for CPE screening, most frequently using chromogenic agar (52%) or MacConkey/CLED agars with carbapenem discs (38%). CONCLUSIONS: The UK prevalence and incidence of clinically significant CPE is currently low, but these MDR bacteria affect most UK regions. Improved IPC measures, vigilance and monitoring 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".