ArticleBMC neurology2024
Exploring the appropriate situation of performing CSF mNGS in patients with proposed intracranial infections.
Article in BMC neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Optimizing metagenomic next-generation sequencing in CNS infections: a diagnostic model based on CSF parameters.Frontiers in cellular and infection microbiology · 2025Article
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5 authors.
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Abstract
backgroundIdentifying the responsible pathogen is crucial for precision medicine in intracranial infections, and Cerebrospinal Fluid (CSF) Metagenomic Next-Generation Sequencing (mNGS) is a reliable method for this detection. However, the indiscriminate utilization of this approach may impose a financial burden on both patients and society. The study aims to investigate the optimal conditions for applying CSF mNGS in patients with suspected intracranial infections, offering valuable references for precision medicine of intracranial infections.
methodsA total of 175 hospitalized patients presenting with suspected intracranial infections were selected for retrospective analysis. Base on the detection of responsible pathogens using CSF mNGS, the patients were categorized into two groups, responsible pathogens in Group A were detected but not in Group B. The types of responsible pathogens in group A and the final diagnosis of patients in group B were analyzed. Demographic data, clinical presentation, CSF analysis, imaging results, and electroencephalography (EEG) findings were analyzed for both groups. Finally, a scoring system was established to promptly assess the appropriateness of CSF mNGS for patients with suspected intracranial infections. Each independent predictor was assigned a score of 1, and the patients were subsequently scored. We advocate sending patients' CSF for mNGS when the cumulative score is ≥ 2.
resultsIn Group A, the predominant responsible pathogen was the varicella-zoster virus (VZV), while Group B exhibited the highest proportion of final diagnoses related to epilepsy. The logistic regression model indicates that headache [OR = 2.982, 95% CI (1.204-7.383), p = 0.018], increased cerebrospinal fluid white cell count [OR = 4.022, 95% CI (1.331-12.156), p = 0.014], and decreased cerebrospinal fluid glucose levels [OR = 9.006, 95% CI (2.778-29.194), P < 0.001] are independent predictive factors for intracranial infection pathogens detected by CSF mNGS. Under this scoring system, the sensitivity for detecting the responsible pathogen was 57.5%, and the specificity was 87.4%.
conclusionThe likelihood of detecting the responsible pathogen through CSF mNGS in patients with suspected intracranial infections can be evaluated using the scoring system. Furthermore, it is crucial to consider the possibility of another condition, such as epilepsy, when the responsible pathogen is not detected using cerebrospinal fluid mNGS.
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