Pan X, Zhang Y, Chen G. The clinical utility of metagenomic next-generation sequencing for the diagnosis of central nervous system infectious diseases.
Neurol Res 2023;
45:919-925. [PMID:
37615407 DOI:
10.1080/01616412.2023.2247299]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/10/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND
To evaluate the clinical utility of metagenomic next-generation sequencing (mNGS) for the diagnosis of central nervous system infections (CNSI).
METHODS
Cerebrospinal fluid (CSF) from 54 patients who were high-level clinical suspicion of CNSI was collected and sent for mNGS and conventional tests from January 2019 to March 2022.
RESULTS
Twenty out of 54 patients were diagnosed with CNSI and 34 non-CNSI. Among the 34 non-CNSI, one was false positive by mNGS. Among the 20 CNSI, 11 had presumed viral encephalitis and/or meningitis, 5 had presumed bacterial meningitis, 2 had presumed TMB, 1 had Crytococcus meningitis and 1 had neurosyphilis. The sensitivity of viral encephalitis and/or meningitis was 0.73 (8/11); 10 virus were detected; 9/10 was dsDNA; 1/10 was ssRNA. SSRN ranged from 1 to 13. The accuracy rate was 0.4, the accuracy rate was positively correlated with SSRN (r = 0.738, P = 0.015), SSRN ≥ 1, the accuracy rate was 0.4; SSRN ≥ 3, the accuracy rate was 0.66; SSRN ≥ 4, the accuracy rate was 0.75; SSRN ≥ 6, the accuracy rate was 1. The sensitivity of bacterial meningitis was 1. Seven kinds of bacteria were detected, among which 3/7 were gram positive, 3/7 were gram negative, and 1/7 was infected NTM (nontuberculous mycobacteria). The accuracy rate was 0.43 (3/7). The sensitivity of TBM was 0.66 (2/3), the accuracy rate was 1. The sensitivity of Crytococcus meningitis was 1, the accuracy rate was 0.5. PPV (positive predictive value) of mNGS was 0.94, NPV (negative predictive value) of mNGS was 0.89, specificity was 0.97 and sensitivity was 0.8. The AUG for CSF mNGS diagnosis of CNSI was 0.89 (95% CI = 0.78-0.99) Headache, meningeal irritation sign and image of meninges abnormal were correlated with the sensitivity of mNGS (r = 0.451, 0.313, 0.446; p = 0.001, 0.021, 0.001); CSF Glucose and CSF Chloride were negatively correlated with sensitivity of mNGS (r = -0.395, -0.462; p = 0.003, < 0.001).
CONCLUSION
mNGS is a detection means with high sensitivity, wide coverage and strong timeliness, which can help clinicians to identify the pathogen diagnosis quickly, conduct targeted anti-infection treatment early and reduce antibiotic abuse. The pathogen which causing low CSF Glucose, low CSF Chloride or meninges infections was more likely to be detected by mNGS. It may be related to growth and structural characteristics of the pathogen and blood-brain barrier damage.
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