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宏基因组学下一代测序技术在中枢神经系统感染诊断中的应用:一项系统评价和荟萃分析

Metagenomics next-generation sequencing for the diagnosis of central nervous system infection: A systematic review and meta-analysis.

作者信息

Qu Chunrun, Chen Yu, Ouyang Yuzhen, Huang Weicheng, Liu Fangkun, Yan Luzhe, Lu Ruoyu, Zeng Yu, Liu Zhixiong

机构信息

Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.

Xiangya School of Medicine, Central South University, Changsha, China.

出版信息

Front Neurol. 2022 Sep 20;13:989280. doi: 10.3389/fneur.2022.989280. eCollection 2022.

Abstract

OBJECTIVE

It is widely acknowledged that central nervous system (CNS) infection is a serious infectious disease accompanied by various complications. However, the accuracy of current detection methods is limited, leading to delayed diagnosis and treatment. In recent years, metagenomic next-generation sequencing (mNGS) has been increasingly adopted to improve the diagnostic yield. The present study sought to evaluate the value of mNGS in CNS infection diagnosis.

METHODS

Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2022 guidelines, we searched relevant articles published in seven databases, including PubMed, Web of Science, and Cochrane Library, published from January 2014 to January 2022. High-quality articles related to mNGS applications in the CNS infection diagnosis were included. The comparison between mNGS and the gold standard of CNS infection, such as culture, PCR or serology, and microscopy, was conducted to obtain true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, which were extracted for sensitivity and specificity calculation.

RESULTS

A total of 272 related studies were retrieved and strictly selected according to the inclusion and exclusion criteria. Finally, 12 studies were included for meta-analysis and the pooled sensitivity was 77% (95% CI: 70-82%, = 39.69%) and specificity was 96% (95% CI: 93-98%, = 72.07%). Although no significant heterogeneity in sensitivity was observed, a sub-group analysis was conducted based on the pathogen, region, age, and sample pretreatment method to ascertain potential confounders. The area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) of mNGS for CNS infection was 0.91 (95% CI: 0.88-0.93). Besides, Deek's Funnel Plot Asymmetry Test indicated no publication bias in the included studies (, > 0.05).

CONCLUSION

Overall, mNGS exhibits good sensitivity and specificity for diagnosing CNS infection and diagnostic performance during clinical application by assisting in identifying the pathogen. However, the efficacy remains inconsistent, warranting subsequent studies for further performance improvement during its clinical application.

STUDY REGISTRATION NUMBER

INPLASY202120002.

摘要

目的

中枢神经系统(CNS)感染是一种伴有多种并发症的严重传染病,这一点已得到广泛认可。然而,当前检测方法的准确性有限,导致诊断和治疗延迟。近年来,宏基因组下一代测序(mNGS)已越来越多地被采用以提高诊断率。本研究旨在评估mNGS在CNS感染诊断中的价值。

方法

按照系统评价和Meta分析的首选报告项目(PRISMA)2022指南,我们检索了2014年1月至2022年1月期间在包括PubMed、科学网和Cochrane图书馆在内的七个数据库中发表的相关文章。纳入了与mNGS在CNS感染诊断中的应用相关的高质量文章。对mNGS与CNS感染的金标准(如培养、PCR或血清学以及显微镜检查)进行比较,以获得真阳性(TP)、真阴性(TN)、假阳性(FP)和假阴性(FN)值,提取这些值用于计算敏感性和特异性。

结果

共检索到272项相关研究,并根据纳入和排除标准进行了严格筛选。最后,纳入12项研究进行Meta分析,合并敏感性为77%(95%CI:70 - 82%,I² = 39.69%),特异性为96%(95%CI:93 - 98%,I² = 72.07%)。尽管在敏感性方面未观察到显著异质性,但基于病原体、地区、年龄和样本预处理方法进行了亚组分析,以确定潜在的混杂因素。mNGS用于CNS感染的汇总受试者工作特征曲线(SROC)的曲线下面积(AUC)为0.91(95%CI:0.88 - 0.93)。此外,Deek漏斗图不对称性检验表明纳入的研究中不存在发表偏倚(P,> 0.05)。

结论

总体而言,mNGS在诊断CNS感染方面表现出良好的敏感性和特异性,并在临床应用中通过协助识别病原体展现出诊断性能。然而,其疗效仍不一致,需要后续研究以在临床应用中进一步提高性能。

研究注册号

INPLASY202120002。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/437e/9530978/1c5c436f6072/fneur-13-989280-g0001.jpg

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