Journal of Translational Medicine · 2020 · 147 citations · 27 references
Life SciencesInfectious DiseasesMicrobiologyDiagnostic MicrobiologyMedical MicrobiologyMetagenomicsBacterial InfectionsCentral Nervous System InfectionsMetagenomic AnalysisPathogen DetectionShotgun Metagenomic SequencingClinical MetagenomicsMcnemar's TestMetagenomic DiagnosisMetagenomic DiagnosticsMetagenomic Pathogen DetectionMetagenomic SequencingNext-generation Sequencing DiagnosisProspective Cohort Data
Abstract Background Accurate etiology diagnosis is crucial for central nervous system infections (CNS infections). The diagnostic value of metagenomic next-generation sequencing (mNGS), an emerging powerful platform, remains to be studied in CNS infections. Methods We conducted a single-center prospective cohort study to compare mNGS with conventional methods including culture, smear and etc. 248 suspected CNS infectious patients were enrolled and clinical data were recorded. Results mNGS reported a 90.00% (9/10) sensitivity in culture-positive patients without empirical treatment and 66.67% (6/9) in empirically-treated patients. Detected an extra of 48 bacteria and fungi in culture-negative patients, mNGS provided a higher detection rate compared to culture in patients with (34.45% vs. 7.56%, McNemar test, p < 0.0083) or without empirical therapy (50.00% vs. 25.00%, McNemar test, p > 0.0083). Compared to conventional methods, positive percent agreement and negative percent agreement was 75.00% and 69.11% separately. mNGS detection rate was significantly higher in patients with cerebrospinal fluid (CSF) WBC > 300 * 10 6 /L, CSF protein > 500 mg/L or glucose ratio ≤ 0.3. mNGS sequencing read is correlated with CSF WBC, glucose ratio levels and clinical disease progression. Conclusion mNGS showed a satisfying diagnostic performance in CNS infections and had an overall superior detection rate to culture. mNGS may held diagnostic advantages especially in empirically treated patients. CSF laboratory results were statistically relevant to mNGS detection rate, and mNGS could dynamically monitor disease progression.
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Amy L. Leber, Kathy Everhart, Joan‐Miquel Balada‐Llasat et al. · Journal of Clinical Microbiology · 2016 · 606 citations · Full text
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