Diagnostic accuracy of VIDISCA-NGS in patients with suspected central nervous system infections

Clinical Microbiology and Infection(2021)

引用 7|浏览22
暂无评分
摘要
Objectives Confirming the diagnosis in viral central nervous system (CNS) infections can be difficult with the currently available diagnostic tools. Virus discovery cDNA-amplified fragment length polymorphism next-generation sequencing (VIDISCA-NGS) is a promising viral metagenomic technique that enables the detection of all viruses in a single assay. We performed a retrospective study on the diagnostic accuracy of VIDISCA-NGS in cerebrospinal fluid (CSF) of individuals with suspected CNS infections. Methods Consecutive adult patients presenting to the Emergency Department or inpatients, who underwent a lumbar puncture for the suspicion of a CNS infection, were included if they were diagnosed with a viral CNS infection, or if a viral CNS infection was initially suspected but eventually a different diagnosis was made. A quantitative PCR panel of the most common causative viruses was performed on CSF of these patients as reference standard and compared with the results of VIDISCA-NGS, the index test. Results We included 38 individuals with viral CNS infections and 35 presenting with suspected CNS infection for whom an alternative aetiology was finally established. Overall sensitivity and specificity were 52% (95% CI 31%–73%) and 100% (95% CI 91%–100%), respectively. One enterovirus, detected by VIDISCA-NGS, was only identified by quantitative PCR upon retesting. Additional viruses identified by VIDISCA-NGS consisted of GB virus C, human papillomavirus, human mastadenovirus C, Merkel cell polyoma virus and anelloviruses. Conclusion In patients for whom routine diagnostics do not yield a causative pathogen, VIDISCA-NGS can be of additional value as it can detect a broader range of viruses, but it does not perform well enough to replace quantitativePCR.
更多
查看译文
关键词
Diagnostic accuracy,Diagnosis,VIDISCA-NGS,Viral central nervous system infections,Viral metagenomics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要