Rapid commercial CTX-M diagnostics: Performance, limitations and clinical impact
Bibliographic record
Abstract
Abstract CTX-M enzymes account for more than 90% of all extended-spectrum β-lactamases (ESBLs) identified in Enterobacterales. Therefore, rapid identification of these enzymes could improve clinical outcomes in patients infected or colonized by such pathogens. In this review, we described the characteristics and limitations of commercially available rapid tests for detecting CTX-M proteins (lateral flow immunoassays) or bla CTX−M genes (microarrays, quantitative PCR, or loop-mediated isothermal amplification). Additionally, we summarized and discussed their potential clinical impact. Some commercial CTX-M assays - particularly those analyzing aliquots from positive blood cultures (i.e., Verigene, BioFire FilmArray, ePlex) - demonstrated clear advantages over standard-of-care methods, shortening the interval to effective therapy and improving overall patient outcomes. However, the widespread adoption of these rapid assays in routine laboratories remains limited due to several factors, including high costs and the lack of robust evidence supporting their positive impact. To address these implementation challenges, laboratories should focus on a defined patient subgroup in whom the application of these assays is likely to yield the greatest clinical impact. In particular, we propose that all laboratories at least perform rapid CTX-M assays on all Gram-negative-positive blood cultures (including those with sterile fluids) obtained from critically ill patients, such as ICU-patients with septic shock. This strategy is best when accompanied by active communication between the laboratory and key stakeholders in patient management. Providing rapid results for this subpopulation of patients may facilitate timely initiation of appropriate therapy and ultimately improve patient outcomes.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".