ArticleArchives of disease in childhood2023
Development of clinical-based scoring system to diagnose tuberculous meningitis in children.
Article in Archives of disease in childhood, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Clinical scoring systems for diagnosing tuberculous meningitis: A systematic review.World journal of critical care medicine · 2026Article
- The diagnostic potential of urine in paediatric patients undergoing initial treatment for tuberculous meningitis.Scientific reports · 2024Article
- The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms.Frontiers in microbiology · 2023Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDiagnosing tuberculous meningitis (TBM) in children is challenging due to the low sensitivity with time delay of bacterial culture techniques and the lack of brain imaging facilities in many low- and middle-income settings. This study aims to establish and test a scoring system consisting of clinical manifestations on history and examination for diagnosing TBM in children.
designA retrospective study was conducted using a diagnostic multivariable prediction model.
participants167 children diagnosed with meningitis (tuberculous, bacterial, viral and others) aged 3 months to 18 years who were hospitalised from July 2011 until November 2021 in a national tertiary hospital in Indonesia.
resultsEight out of the 10 statistically significant clinical characteristics were used to develop a predictive model. These resulted in good discrimination and calibration variables, which divided into systemic features with a cut-off score of ≥3 (sensitivity 78.8%; specificity 86.6%; the area under the curve (AUC) value 0.89 (95% CI 0.85 to 0.95; p<0.001)) and neurological features with a cut-off score of ≥2 (sensitivity 61.2%; specificity 75.2%; the AUC value 0.73 (95% CI 0.66 to 0.81; p<0.001)). Combined together, this scoring system predicted the diagnosis of TBM with a sensitivity, specificity and positive predictive value of 47.1%, 95.1% and 90.9%, respectively.
conclusionThe clinical scoring system consisting of systemic and neurological features can be used to predict the diagnosis of TBM in children with limited resource setting. The scoring system should be assessed in a prospective cohort.
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