BMC Infectious Diseases (Dec 2021)

The microbiological diagnostic performance of metagenomic next-generation sequencing in patients with sepsis

  • Di Ren,
  • Chao Ren,
  • Renqi Yao,
  • Lin Zhang,
  • Xiaomin Liang,
  • Guiyun Li,
  • Jiaze Wang,
  • Xinke Meng,
  • Jia Liu,
  • Yu Ye,
  • Haoli Li,
  • Sha Wen,
  • Yanhong Chen,
  • Dan Zhou,
  • Xisi He,
  • Xiaohong Li,
  • Kai Lai,
  • Ying Li,
  • Shuiqing Gui

DOI
https://doi.org/10.1186/s12879-021-06934-7
Journal volume & issue
Vol. 21, no. 1
pp. 1 – 9

Abstract

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Abstract Background In this study, we aimed to perform a comprehensive analysis on the metagenomic next-generation sequencing for the etiological diagnosis of septic patients, and further to establish optimal read values for detecting common pathogens. Methods In this single-center retrospective study, septic patients who underwent pathogen detection by both microbial culture and metagenomic next-generation sequencing in the intensive care unit of the Second People’s Hospital of Shenzhen from June 24, 2015, to October 20, 2019, were included. Results A total of 193 patients with 305 detected specimens were included in the final analysis. The results of metagenomic next-generation sequencing showed significantly higher positive rates in samples from disparate loci, including blood, bronchoalveolar lavage fluid, and cerebrospinal fluid, as well as in the determination of various pathogens. The optimal diagnostic reads were 2893, 1825.5, and 892.5 for Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae, respectively. Conclusions The metagenomic next-generation sequencing is capable of identifying multiple pathogens in specimens from septic patients, and shows significantly higher positive rates than culture-based diagnostics. The optimal diagnostic reads for frequently detected microbes might be useful for the clinical application of metagenomic next-generation sequencing in terms of timely and accurately determining etiological pathogens for suspected and confirmed cases of sepsis due to well-performed data interpretation.

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