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Evaluation of Autof MS 1000 and Vitek MS MALDI-TOF MS System in Identification of Closely-Related Yeasts Causing Invasive Fungal Diseases

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单位: [1]Department of Laboratory Medicine, and Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China [2]Graduate School, Peking Union Medical College, Beijing, China [3]Department of Infectious Diseases and Clinical Microbiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China [4]Department of Laboratory Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China [5]Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, New South Wales Health Pathology, Westmead Hospital, The University of Sydney, Westmead, NSW, Australia [6]Department of Clinical Laboratory, Charles Sturt University, Orange, NSW, Australia [7]New South Wales Health Pathology, Regional and Rural, Orange Hospital, NSW, Australia
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关键词: MALDI-TOF MS closely-related yeasts invasive fungal diseases Autof MS 1000 Vitek MS

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Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been accepted as a rapid, accurate, and less labor-intensive method in the identification of microorganisms in clinical laboratories. However, there is limited data on systematic evaluation of its effectiveness in the identification of phylogenetically closely-related yeast species. In this study, we evaluated two commercially available MALDI-TOF systems, Autof MS 1000 and Vitek MS, for the identification of yeasts within closely-related species complexes. A total of 1,228 yeast isolates, representing 14 different species of five species complexes, including 479 of Candida parapsilosis complex, 323 of Candida albicans complex, 95 of Candida glabrata complex, 16 of Candida haemulonii complex (including two Candida auris), and 315 of Cryptococcus neoformans complex, collected under the National China Hospital Invasive Fungal Surveillance Net (CHIF-NET) program, were studied. Autof MS 1000 and Vitek MS systems correctly identified 99.2% and 89.2% of the isolates, with major error rate of 0.4% versus 1.6%, and minor error rate of 0.1% versus 3.5%, respectively. The proportion of isolates accurately identified by Autof MS 1000 and Vitek MS per each yeast complex, respectively, was as follows; C. albicans complex, 99.4% vs 96.3%; C. parapsilosis complex, 99.0% vs 79.1%; C glabrata complex, 98.9% vs 94.7%; C. haemulonii complex, 100% vs 93.8%; and C. neoformans, 99.4% vs 95.2%. Overall, Autof MS 1000 exhibited good capacity in yeast identification while Vitek MS had lower identification accuracy, especially in the identification of less common species within phylogenetically closely-related species complexes.

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出版当年[2020]版:
大类 | 2 区 医学
小类 | 3 区 免疫学 3 区 微生物学
最新[2025]版:
大类 | 2 区 医学
小类 | 2 区 免疫学 2 区 微生物学
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出版当年[2019]版:
Q2 IMMUNOLOGY Q2 MICROBIOLOGY
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Q1 MICROBIOLOGY Q2 IMMUNOLOGY

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第一作者单位: [1]Department of Laboratory Medicine, and Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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