ArticleBMC infectious diseases2023
Development and validation of a new model for the early diagnosis of tuberculous meningitis in adults based on simple clinical and laboratory parameters.
Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Diagnosis of Tuberculous Meningitis: Integrating Clinical Assessment and Molecular Diagnostics.Diagnostics (Basel, Switzerland) · 2026Review
- Q fever sternum osteomyelitis in a patient with history of cardiovascular surgery: A case report from China.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- A novel diagnostic strategy of differential diagnosis of tuberculous meningitis and non-tuberculous meningitis: a retrospective observational cohort study.Microbiology spectrum · 2026Observational
- Construction and verification of early diagnosis model of tuberculous meningitis.Frontiers in cellular and infection microbiology · 2026Article
- Tuberculous Meningitis Across the Lifespan.The Journal of infectious diseases · 2025Review
- Chronic meningitis: diagnostic and therapeutic challenges.Current opinion in infectious diseases · 2025Review
- Construction of a nomogram model based on multiple factors to differentiate cryptococcal meningitis from tuberculous meningitis in HIV-Infected patients.Scientific reports · 2025Article
- Development and validation of a diagnostic model for tuberculous meningitis based on laboratory data.Frontiers in cellular and infection microbiology · 2025Article
- A Retrospective Analysis of Central and Peripheral Metabolic Characteristics in Patients with Cryptococcal Meningitis.Neurology and therapy · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe differential diagnosis between tuberculous meningitis (TBM) and viral meningitis (VM) or bacterial meningitis (BM) remains challenging in clinical practice, particularly in resource-limited settings. This study aimed to establish a diagnostic model that can accurately and early distinguish TBM from both VM and BM in adults based on simple clinical and laboratory parameters.
methodsPatients diagnosed with TBM or non-TBM (VM or BM) between January 2012 and October 2021 were retrospectively enrolled from the General Hospital (derivation cohort) and Branch Hospital (validation cohort) of Ningxia Medical University. Demographic characteristics, clinical symptoms, concomitant diseases, and cerebrospinal fluid (CSF) parameters were collated. Univariable logistic analysis was performed in the derivation cohort to identify significant variables (P < 0.05). A multivariable logistic regression model was constructed using these variables. We verified the performance including discrimination, calibration, and applicability of the model in both derivation and validation cohorts.
resultsA total of 222 patients (70 TBM and 152 non-TBM [75 BM and 77 VM]) and 100 patients (32 TBM and 68 non-TBM [31 BM and 37 VM]) were enrolled as derivation and validation cohorts, respectively. The multivariable logistic regression model showed that disturbance of consciousness for > 5 days, weight loss > 5% of the original weight within 6 months, CSF lymphocyte ratio > 50%, CSF glucose concentration < 2.2 mmol/L, and secondary cerebral infarction were independently correlated with the diagnosis of TBM (P < 0.05). The nomogram model showed excellent discrimination (area under the curve 0.959 vs. 0.962) and great calibration (P-value in the Hosmer-Lemeshow test 0.128 vs. 0.863) in both derivation and validation cohorts. Clinical decision curve analysis showed that the model had good applicability in clinical practice and may benefit the entire population.
conclusionsThis multivariable diagnostic model may help clinicians in the early discrimination of TBM from VM and BM in adults based on simple clinical and laboratory parameters.
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