Detection of ESBL-producing <i>Klebsiella oxytoca</i> complex with VITEK 2 system and screening cutoffs for implementing confirmatory tests
Bibliographic record
Abstract
ABSTRACT Klebsiella oxytoca complex ( Ko C) are important nosocomial pathogens that can be reservoirs of transmissible extended-spectrum β-lactamase (ESBL) genes. Therefore, it is essential for clinical microbiology laboratories to distinguish between Ko C producing ESBLs (ESBL- Ko C) and those hyperproducing the natural OXY-type β-lactamases (hOXY- Ko C). We investigated the abilities of VITEK 2 with and without using the Advanced Expert System (AES) to detect ESBL producers among 44 well-characterized Ko C strains (including 11 ESBL- Ko C and 21 hOXY- Ko C). VITEK 2/AES showed 100% sensitivity (Se) and 64.7% specificity (Sp), whereas the VITEK 2 coupled by the Clinical Laboratory Standards Institute (CLSI) ESBL confirmatory tests (ESBL-CTs; i.e., disk-combination tests) showed 100% Se and 97.5% Sp to detect ESBL- Ko C. We also analyzed Ko C-specific screening cutoffs for ceftriaxone (CRO), cefpodoxime (CPD), ceftazidime (CAZ), cefotaxime (CTX), and aztreonam (ATM) to negate unnecessary ESBL-CTs. As a result, we propose the following screening cutoffs (minimum inhibitory concentration [MIC] and inhibition zone diameter): CRO, >4 µg/mL and ≤16 mm; CPD, >4 µg/mL and ≤10 mm; CAZ, >1 µg/mL and ≤22 mm (European Committee on Antimicrobial Susceptibility Testing [EUCAST] disk)/≤30 mm (CLSI disk); CTX, >4 µg/mL and ≤12 mm (EUCAST disk)/≤22 mm (CLSI disk); ATM, >1 µg/mL and ≤28 mm. Notably, all suggested cutoffs could assure 100% Se and high Sp/positive predictive values for our 44 Ko C strains. In conclusion, the AES performed poorly, while VITEK 2 with the CLSI ESBL-CTs yielded a reliable methodology to distinguish ESBL- Ko C from hOXY- Ko C. This study also proposed revised screening cutoffs for detecting ESBL- Ko C and reducing the unnecessary use of ESBL-CTs. IMPORTANCE Species within the Klebsiella oxytoca complex ( Ko C) are emerging clinical pathogens of increasing concern. These bacteria can acquire plasmid-mediated ESBL genes, seriously complicating antibiotic treatment and overall management of infected patients. Differentiating ESBL-producing from non-ESBL-producing Ko C isolates is therefore crucial. However, this task presents significant challenges for clinical laboratories. In this work, we showed that the automated VITEK 2 system equipped with its AES fails to differentiate the two groups of Ko C isolates. In contrast, VITEK 2 alone followed by the ESBL screen and phenotypic confirmatory tests provides accurate differentiation. Since this latter approach increases the diagnostic workload, we also proposed new screening cutoffs for key cephalosporins that may reduce the current high number of unnecessary confirmatory tests.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".