What Is the Appropriate Meropenem MIC for Screening of Carbapenemase-Producing Enterobacteriaceae in Low-Prevalence Settings?
Bibliographic record
Abstract
Infection with carbapenemase-producing Enterobacteriaceae (CPE) has been shown to cause significant illness among hospitalized patients. Given the paucity of treatment options, there is a critical need to stop the spread of CPE. However, screening for the presence of CPE in laboratory settings has been challenging. In order to assess the effectiveness of current CPE detection guidelines, we analyzed the meropenem MIC distribution for a large set of clinical Enterobacteriaceae isolates. A total of 1,022 isolates submitted to the Public Health Ontario Laboratories (PHOL) from January 2011 to March 2014 were examined. Only isolates displaying a meropenem or ertapenem MIC of ≥ 0.25 or ≥ 1 μg/ml, respectively, were included. Carbapenemase-positive isolates were identified by multiplex PCR. We identified 189 isolates positive for carbapenemases, which primarily comprised NDM, KPC, and OXA-48-like carbapenemases, and these isolates were largely Klebsiella spp., Escherichia coli, and Enterobacter spp. Interestingly, 14 to 20% of these isolates displayed meropenem MICs within the susceptible range on the basis of CLSI and EUCAST breakpoint interpretive criteria. While the majority of meropenem-susceptible CPE isolates were observed to be E. coli, meropenem susceptibility was not exclusive to any one species/genus or carbapenemase type. Application of CLSI screening recommendations captured only 86% of carbapenemase-producing isolates, whereas application of EUCAST recommendations detected 98.4% of CPE isolates. In a region with a low carbapenemase prevalence, meropenem-based screening approaches require a cutoff MIC near the epidemiological wild-type threshold in order to achieve nearly optimal CPE identification.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".