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Development and validation of a predictive model for 30-day mortality in adult bacterial meningitis: a retrospective

Jun Zhou1,2, Jicheng Xing3, Xiangjun Cheng1,2

  • 1Department of Laboratory Medicine, The First Affiliated Hospital With Nanjing Medical University, Nanjing, China.

Frontiers in Medicine
|December 8, 2025
PubMed
Abstract

Insights

A new nomogram accurately predicts mortality in adult bacterial meningitis patients. This tool uses age, neurological complications, multidrug-resistant infections, cerebrospinal fluid neutrophils, and C-reactive protein to identify high-risk individuals for better treatment.

Area of Science:

  • Infectious Diseases
  • Clinical Medicine
  • Epidemiology

Background:

  • Bacterial meningitis remains a significant cause of mortality despite advancements in antimicrobial therapy.
  • Early identification of high-risk patients is a persistent challenge in clinical practice.
  • Developing effective risk stratification tools is crucial for improving patient outcomes.

Purpose of the Study:

  • To develop and validate a predictive model for identifying high-risk adult patients with bacterial meningitis.
  • To incorporate readily available clinical and laboratory parameters into a user-friendly tool.
  • To aid clinicians in making timely and informed treatment decisions.

Main Methods:

  • Retrospective analysis of 277 adult bacterial meningitis patients (2016-2024).
  • Patients were divided into training (n=194) and validation (n=83) cohorts.
  • Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable regression were used to construct a predictive nomogram, with performance assessed by AUC and decision curve analysis.

Main Results:

  • The overall 30-day mortality rate was 29.2%.
  • Independent predictors of mortality included age, neurological complications, multidrug-resistant (MDR) infection, cerebrospinal fluid neutrophil percentage, and serum C-reactive protein.
  • The nomogram demonstrated good discrimination, with AUCs of 0.851 (training) and 0.814 (validation).

Conclusions:

  • A validated prediction model incorporating accessible clinical and laboratory data offers accurate risk stratification for adult bacterial meningitis.
  • This nomogram can assist clinicians in identifying high-risk patients who may benefit from intensified monitoring and treatment.
  • The tool has confirmed clinical utility across a broad range of probability thresholds.
Keywords:
bacterial meningitismortalitynomogramprediction modelrisk factors

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