ORIGINAL RESEARCH article
Front. Med.
Sec. Infectious Diseases: Pathogenesis and Therapy
Development and Validation of a Predictive Model for 30-Day Mortality in Adult Bacterial Meningitis: A Retrospective Cohort Study
Provisionally accepted
- 1The First Affiliated Hospital With Nanjing Medical University, Nanjing, China
- 2Jingling Hospital, Nanjing, China
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Background: Bacterial meningitis continues to carry significant mortality despite advances in antimicrobial therapy. Early identification of high-risk patients remains challenging in clinical practice. Methods: We conducted a retrospective analysis of 277 adult patients with bacterial meningitis admitted between 2016 and 2024. Patients were randomly allocated to training (n=194) and validation (n=83) cohorts. Comprehensive clinical parameters, laboratory findings (including cerebrospinal fluid analysis), and microbiological data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable regression were used to construct a predictive nomogram. Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. Results: The overall 30-day mortality rate was 29.2% (81/277). In multivariate analysis, five independent predictors emerged: age (Hazard Ratio (HR) 1.04, 95% Confidence Interval (CI) 1.01-1.08), neurological complications (HR 2.31, 95% CI 1.12-4.78), multidrug-resistant (MDR) infection (HR 3.15, 95% CI 1.42-6.99), cerebrospinal fluid neutrophil percentage (HR 1.03, 95% CI 1.01-1.05), and serum C-reactive protein (HR 1.12, 95% CI 1.03-1.22). The nomogram demonstrated good discrimination with AUCs of 0.851 (95% CI 0.793-0.909) in the training cohort and 0.814 (95% CI 0.715-0.914) in validation. Decision curve analysis confirmed clinical utility across a wide probability threshold range. Conclusion: Our validated prediction model incorporating readily available clinical and laboratory parameters provides accurate risk stratification for adult bacterial meningitis patients. This tool may assist clinicians in identifying high-risk individuals who could benefit from more intensive monitoring and treatment strategies.
Keywords: Bacterial meningitis, Mortality, Prediction model, nomogram, Risk factors
Received: 27 Aug 2025; Accepted: 29 Oct 2025.
Copyright: © 2025 Zhou, Xing, Cheng, Jin and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jun Zhou, zhoujun5958@163.com
Yiling Huang, yilinghuang10@126.com
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