Change in the Antimicrobial Resistance Profile of Extended-Spectrum β-Lactamase-Producing <i>Escherichia coli</i>
Bibliographic record
Abstract
Background: This study aimed to investigate the trends and antimicrobial resistance profile of extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-EC) clinical isolates. Methods: A total of 1,303 E. coli isolates from January 2012 to December 2017 at Fukuoka University Chikushi Hospital, Japan, were analyzed. The rate of resistance to cefmetazole (CMZ), flomoxef (FMOX), imipenem (IPM), meropenem (MEPM), amikacin (AMK), gentamicin (GM), minocycline (MINO), ciprofloxacin (CPFX), and levofloxacin (LVFX) was compared between non-ESBL-producing E. coli (non-ESBL-EC) and ESBL-EC. Results: The proportion of ESBL-EC among all the E. coli isolates was 24.6% (320/1,303), and the proportion remained stable throughout the study period. There was no difference in the rate of resistance to CMZ, FMOX, IPM, MEPM, and AMK between non-ESBL-EC and ESBL-EC; however, the rate of resistance to GM, MINO, CPFX, and LVFX was higher in ESBL-EC than in non-ESBL-EC (17.5% vs. 10.0%, 19.1% vs. 7.7%, 87.5% vs. 24.2%, and 87.5% vs. 23.5%, respectively; P < 0.01). The rate of resistance to CPFX and LVFX in ESBL-EC increased throughout the study course. The rate of E. coli isolates susceptible to all the antibiotics was significantly higher in non-ESBL-EC than in ESBL-EC (68.2% vs. 7.5%; P < 0.01), and this rate decreased significantly from 10.0% in 2012 to 3.8% in 2017 in ESBL-EC (P < 0.01). Conclusions: Our findings indicate a changing antimicrobial resistance profile of ESBL-EC, particularly to fluoroquinolones. Determination of the prevalence and antimicrobial resistance of ESBL-EC will help physicians in selecting the initial empirical treatment for patients with ESBL-EC infections. J Clin Med Res. 2019;11(9):635-641 doi: https://doi.org/10.14740/jocmr3928
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".