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Updated: Jul 18, 2026

A Choroid Plexus Epithelial Cell-based Model of the Human Blood-Cerebrospinal Fluid Barrier to Study Bacterial Infection from the Basolateral Side
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A Nomogram Model to Predict Meningitis Occurrence in Cerebrospinal Fluid Leak Patients

Ru Tang1, Song Mao1, Yuelong Gu1

  • 1Department of Otolaryngology Head and Neck Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

The Laryngoscope
|September 9, 2025
PubMed
Abstract

Insights

Meningitis risk after cerebrospinal fluid (CSF) leaks is high. This study identified pneumonia and iatrogenic CSF leaks as key risk factors, developing a predictive model for early detection.

Area of Science:

  • Neurosurgery
  • Infectious Disease Epidemiology
  • Medical Informatics

Background:

  • Meningitis post-cerebrospinal fluid (CSF) leak presents significant morbidity and mortality.
  • Current management lacks consensus due to inadequate risk stratification tools.
  • Early detection of meningitis in CSF leak patients is crucial.

Purpose of the Study:

  • Identify determinants of meningitis risk in patients with CSF leaks.
  • Develop and validate a predictive model for early meningitis detection.

Main Methods:

  • 179 patients with CSF leaks were randomized into training and validation sets.
  • LASSO regression and multivariate logistic regression identified risk factors.
  • Model accuracy was assessed using ROC curves, calibration plots, and DCA.

Main Results:

  • 22.3% of patients developed meningitis; males predominated.
  • Pneumonia and iatrogenic CSF leaks were independent risk factors.
  • A nomogram incorporating smoking, sex, pneumonia, and etiology demonstrated strong predictive performance (AUC 0.75-0.76).

Conclusions:

  • Pneumonia and iatrogenic CSF leaks are significant risk factors for meningitis.
  • The developed nomogram effectively predicts meningitis risk in CSF leak patients.
  • This tool aids in early evaluation and management of at-risk individuals.
Keywords:
CSF leakmeningitisprediction modelrisk factor

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