Metagenomic Next-Generation Sequencing Direct Diagnosis of Mixed Bacterial Meningitis in a Child: A Case Report

Wei Wang1, Yujuan Wang1, Xiaowei Xin1

  • 1Department of Pediatric Intensive Care Unit, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, People's Republic of China.

PubMed
Abstract

Insights

Metagenomic sequencing rapidly diagnosed rare mixed bacterial meningitis in a child with a history of neurosurgery. This advanced technique identified anaerobic bacteria, guiding effective long-term treatment and recovery.

Area of Science:

  • Pediatric infectious diseases
  • Microbiology
  • Genomics

Background:

  • Bacterial meningitis poses significant global health risks, particularly in children.
  • Mixed bacterial meningitis is uncommon and challenging to diagnose due to difficulties in identifying anaerobic pathogens with conventional methods.
  • Neurosurgical interventions are identified as risk factors for cerebral infections like meningitis.

Observation:

  • A case of polymicrobial meningitis in a 35-month-old male child with a history of tethered cord syndrome (TCS) surgery is presented.
  • Metagenomic next-generation sequencing (mNGS) was employed for diagnosis.
  • The patient underwent a detethering procedure for TCS.

Findings:

  • mNGS analysis of cerebrospinal fluid (CSF) identified six bacterial species, including four anaerobic bacteria.
  • Magnetic resonance imaging (MRI) revealed abnormalities in the lumbosacral area.
  • The child recovered following extended antibiotic therapy and surgical intervention.

Implications:

  • This case highlights the utility of mNGS in rapidly and accurately diagnosing mixed bacterial meningitis, especially in identifying rare anaerobic pathogens.
  • For patients with a history of neurosurgery and severe brain infections, considering mixed infections and anaerobic pathogens is crucial.
  • Empiric antibiotic therapy should account for potential polymicrobial involvement in susceptible patients.

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