Metagenomic Next-generation Sequencing for Pathogen Identification in Bronchoalveolar Lavage Fluid from Neonates Receiving Extracorporeal Membrane Oxygenation.
The Pediatric Infectious Disease Journal(2024)
摘要
BACKGROUND:Neonates on extracorporeal membrane oxygenation (ECMO) are at high risk of infection. Rapid and accurate identification of pathogens is essential to improve the prognosis of children on ECMO. Metagenome next-generation sequencing (mNGS) has been used in recent years to detect pathogenic bacteria, but evidence for its use in neonates on ECMO is lacking.METHODS:This retrospective study was conducted using an electronic medical record system. We analyzed the results of mNGS and conventional microbiological tests (CMTs) in bronchoalveolar lavage fluid of neonates receiving ECMO support with pulmonary infections in our hospital from July 2021 to January 2023.RESULTS:We screened 18 ECMO-supported neonates with pneumonia for inclusion in the study. The median age of the included children was 2 (1-4) days, the median gestational age was 38.3 (33-40 +4 ) weeks, and the median weight was 3.3 (2.2-4.8) kg. The detection rate of mNGS was 77.8% (14/18), higher than the 44.4% (8/18) of CMT ( P = 0.04). A total of 20 pathogens were detected in mNGS, with the top 3 most common pathogens being Klebsiella pneumoniae , Acinetobacter baumannii and Escherichia coli . Mixed infections were found in 14 cases (77.8%), including 13 cases (72.2%) with mixed infections detected by mNGS and 7 cases (27.8%) with mixed infections detected by CMT. A total of 9 children underwent treatment changes based on mNGS results and all of them experienced relief of symptoms.CONCLUSION:Compared with CMT, mNGS can detect pathogens earlier and more sensitively, and may play an important role in ECMO-supported neonatal pneumonia pathogen detection and optimization of antibiotic therapy.
更多查看译文
关键词
metagenomic next-generation sequencing,extracorporeal membrane oxygenation,bronchoalveolar lavage fluid,pneumonia,neonate
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn