Interlaboratory Variability of Caspofungin MICs for Candida spp. Using CLSI and EUCAST Methods: Should the Clinical Laboratory Be Testing This Agent?
Bibliographic record
Abstract
Although Clinical and Laboratory Standards Institute (CLSI) clinical breakpoints (CBPs) are available for interpreting echinocandin MICs for Candida spp., epidemiologic cutoff values (ECVs) based on collective MIC data from multiple laboratories have not been defined. While collating CLSI caspofungin MICs for 145 to 11,550 Candida isolates from 17 laboratories (Brazil, Canada, Europe, Mexico, Peru, and the United States), we observed an extraordinary amount of modal variability (wide ranges) among laboratories as well as truncated and bimodal MIC distributions. The species-specific modes across different laboratories ranged from 0.016 to 0.5 μg/ml for C. albicans and C. tropicalis, 0.031 to 0.5 μg/ml for C. glabrata, and 0.063 to 1 μg/ml for C. krusei. Variability was also similar among MIC distributions for C. dubliniensis and C. lusitaniae. The exceptions were C. parapsilosis and C. guilliermondii MIC distributions, where most modes were within one 2-fold dilution of each other. These findings were consistent with available data from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) (403 to 2,556 MICs) for C. albicans, C. glabrata, C. krusei, and C. tropicalis. Although many factors (caspofungin powder source, stock solution solvent, powder storage time length and temperature, and MIC determination testing parameters) were examined as a potential cause of such unprecedented variability, a single specific cause was not identified. Therefore, it seems highly likely that the use of the CLSI species-specific caspofungin CBPs could lead to reporting an excessive number of wild-type (WT) isolates (e.g., C. glabrata and C. krusei) as either non-WT or resistant isolates. Until this problem is resolved, routine testing or reporting of CLSI caspofungin MICs for Candida is not recommended; micafungin or anidulafungin data could be used instead.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".