Evidence map›Paper›PMID 41357526›Full record

ArticleFrontiers in medicine2025

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

Jun Zhou, Jicheng Xing, Xiangjun Cheng, Fei Jin, Yiling Huang

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Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Jun Zhou *Department of Laboratory Medicine, The First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
Jicheng Xing *Institute of Laboratory Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Xiangjun Cheng *Department of Laboratory Medicine, The First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
Fei JinDepartment of Laboratory Medicine, The First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
Yiling HuangDepartment of Laboratory Medicine, The First Affiliated Hospital With Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 ( 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.

Indexed as

bacterial meningitismortalitynomogramprediction modelrisk factors

Identifiers

PMID41357526
PMCPMC12675203

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