ArticleFrontiers in cellular and infection microbiology2025
Optimizing metagenomic next-generation sequencing in CNS infections: a diagnostic model based on CSF parameters.
Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to assess the association between routine cerebrospinal fluid (CSF) biochemical parameters and metagenomic next-generation sequencing (mNGS) results, and to develop a predictive model to optimize mNGS testing strategies in patients with suspected central nervous system (CNS) infections. Methods: We retrospectively enrolled 110 patients with suspected CNS infections between December 2019 and January 2024. All underwent both CSF analysis and mNGS testing. Patients were divided into mNGS-positive (n = 62) and negative (n = 48) groups. Logistic regression identified independent predictors, and a nomogram was constructed based on CSF cell count and protein concentration. Model performance was assessed via receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation included 10-fold cross-validation and 1000-sample bootstrap. An external validation was performed using a cohort of 40 patients enrolled from another hospital campus (May-October 2024). The derivation cohort was retrospectively collected, whereas the external validation cohort was prospectively enrolled. Results: mNGS positivity rate was 56.36%, significantly higher than CSF culture (6.36%), with an overall diagnostic concordance of 79.09%. Compared to the mNGS-negative group, positive patients had significantly higher CSF cell counts, protein levels, turbidity, ICU admission (ICUA), antimicrobial regimen adjustment (AAR), and mortality, while glucose was significantly lower ( Conclusion: CSF cell count and protein are reliable predictors of mNGS positivity. The model for practice showed consistent diagnostic performance and may aid in guiding precision mNGS testing, particularly in resource-constrained settings.
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