An interlaboratory study using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) to determine the identification of clinically important yeasts (n = 35) was performed at 11 clinical centers, one company, and one reference center using the Bruker Daltonics MALDI Biotyper system. The optimal cutoff for the MALDI-TOF MS score was investigated using receiver operating characteristic (ROC) curve analyses. The percentages of correct identifications were compared for different sample preparation methods and different databases. Logistic regression analysis was performed to analyze the association between the number of spectra in the database and the percentage of strains that were correctly identified. A total of 5,460 MALDI-TOF MS results were obtained. Using all results, the area under the ROC curve was 0.95 (95% confidence interval [CI], 0.94 to 0.96). With a sensitivity of 0.84 and a specificity of 0.97, a cutoff value of 1.7 was considered optimal. The overall percentage of correct identifications (formic acid-ethanol extraction method, score ≥ 1.7) was 61.5% when the commercial Bruker Daltonics database (BDAL) was used, and it increased to 86.8% by using an extended BDAL supplemented with a Centraalbureau voor Schimmelcultures (CBS)-KNAW Fungal Biodiversity Centre in-house database (BDAL+CBS in-house). A greater number of main spectra (MSP) in the database was associated with a higher percentage of correct identifications (odds ratio [OR], 1.10; 95% CI, 1.05 to 1.15; P < 0.01). The results from the direct transfer method ranged from 0% to 82.9% correct identifications, with the results of the top four centers ranging from 71.4% to 82.9% correct identifications. This study supports the use of a cutoff value of 1.7 for the identification of yeasts using MALDI-TOF MS. The inclusion of enough isolates of the same species in the database can enhance the proportion of correctly identified strains. Further optimization of the preparation methods, especially of the direct transfer method, may contribute to improved diagnosis of yeast-related infections.

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Comparative Study | Evaluation Study | Journal Article | Research Support, Non-U.S. Gov't

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Vlek A, Kolecka A, Khayhan K, Theelen B, Groenewald M, Boel E, Multicenter Study Group, Boekhout T

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