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

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.