Diagnostic performance of metagenomic next-generation sequencing for the detection of pathogens in cerebrospinal fluid in pediatric patients with central nervous system infection: a systematic review and meta-analysis

Author:

He SikeORCID,Xiong YingORCID,Tu Teng,Feng Jiaming,Fu Yu,Hu Xu,Wang Neng,Li DapengORCID

Abstract

Abstract Background Detecting pathogens in pediatric central nervous system infection (CNSI) is still a major challenge in medicine. In addition to conventional diagnostic patterns, metagenomic next-generation sequencing (mNGS) shows great potential in pathogen detection. Therefore, we systematically evaluated the diagnostic performance of mNGS in cerebrospinal fluid (CSF) in pediatric patients with CNSI. Methods Related literature was searched in the Web of Science, PubMed, Embase, and Cochrane Library. We screened the literature and extracted the data according to the selection criteria. The quality of included studies was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the certainty of the evidence was measured by the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) score system. Then, the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odd’s ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve (sROC) were estimated in Stata Software and MetaDisc. Subgroup analyses were performed to investigate the potential factors that influence the diagnostic performance. Results A total of 10 studies were included in the meta-analysis. The combined sensitivity was 0.68 (95% confidence interval [CI]: 0.59 to 0.76, I2 = 66.77%, p < 0.001), and the combined specificity was 0.89 (95% CI: 0.80 to 0.95, I2 = 83.37%, p < 0.001). The AUC of sROC was 0.85 (95% CI, 0.81 to 0.87). The quality level of evidence elevated by the GRADE score system was low. Conclusions Current evidence shows that mNGS presents a good diagnostic performance in pediatric CNSI. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.

Publisher

Springer Science and Business Media LLC

Subject

Infectious Diseases

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3