Antimicrobial susceptibility and distribution trends among AmpC-producing Enterobacterales at a regional health authority in British Columbia: a 5-year retrospective review
Bibliographic record
Abstract
Introduction: Existing literature supports using cefepime as a carbapenem-sparing agent to treat AmpC-producing Enterobacterales infections. To characterize existing susceptibility trends, we conducted a 5-year retrospective study assessing minimum inhibitory concentration (MIC) distributions for ceftriaxone, cefepime, piperacillin-tazobactam, and meropenem across Vancouver Island Health Authority (Vancouver Island, British Columbia, Canada). Methods: MIC data for AmpC isolates between November 2019 to October 2024 were retrieved (BD Epicenter™). Blood cultures, invasive specimens, urine and miscellaneous samples (e.g., respiratory, wounds) were included; we excluded surveillance specimens. MIC values were interpreted based upon CLSI M100-E34 breakpoints (Table 2A-1). Descriptive statistics were computed and compared between moderate-risk AmpC inducers (MRAC) versus low-risk AmpC inducers (LRAC). Results: 5,809 isolates were analyzed, with 3,646 (62.8%) identified as MRAC. Across all organisms, susceptibility rates for ceftriaxone, piperacillin-tazobactam, cefepime, and meropenem were 81.6%, 87.4%, 95.5%, and 98.7% respectively. Compared to LRAC, MRAC organisms demonstrated lower susceptibility to ceftriaxone (76.0% versus 91.1%) and piperacillin-tazobactam (81.5% versus 97.3%). Cefepime and meropenem demonstrated similar susceptibility rates between LRAC and MRAC. MIC90 for cefepime was 1 µg/ml for all species except E. cloacae (MIC90 4µg/ml). Conclusion: Between 2019-2024, 95.5% of our isolates demonstrated susceptibility to cefepime within our local health authority. Further research will correlate patient outcomes and establish MIC thresholds to guide routine testing and clinical use of cefepime.
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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 | 0.001 | 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".