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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".