Comparison of four automated microbiology systems with 16S rRNA gene sequencing for identification of Chryseobacterium and Elizabethkingia species

作者：Lin Jiun Nong^*; Lai Chung Hsu; Yang Chih Hui; Huang Yi Han; Lin Hsiu Fang; Lin Hsi Hsun

来源：Scientific Reports, 2017, 7(1): 13824.

DOI：10.1038/s41598-017-14244-9

Chryseobacterium and Elizabethkingia species have recently emerged as causative agents in life-threatening infections in humans. We aimed to evaluate the rates at which four common microbial identification systems identify Chryseobacterium and Elizabethkingia species in clinical microbiology laboratories. Based on the results of 16S rRNA gene sequencing, a total of 114 consecutive bacteremic isolates, including 36 (31.6%) C. indologenes, 35 (30.7%) E. anophelis, 22 (19.3%) C. gleum, 13 (11.4%) E. meningoseptica, and other species, were included in this study. The overall concordance between each method and 16S rRNA gene sequencing when identifying Chryseobacterium and Elizabethkingia species was 42.1% for API/ID32, 41.2% for Phoenix 100 ID/AST, 43.9% for VITEK 2, and 42.1% for VITEK MS. Among the 22 C. gleum isolates, only one (4.8%) was correctly identified using VITEK 2 and Phoenix 100 ID/AST, and none were accurately recognized using API/ID32 or VITEK MS. Except for two isolates that were not identified using API/ID32, all E. anophelis isolates were misidentified by all four identification systems as E. meningoseptica. Our results show that these approaches have low accuracy when identifying Chryseobacterium and Elizabethkingia species. Hence, we recommend amending the discrimination rate of and adding non-claimed pathogens to databases of microbial identification systems.

• 出版日期2017-10-23