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Войти в исследования Frontiers in cellular and infection microbiology · 2026
Construction and verification of early diagnosis model of tuberculous meningitis.
СтатьяНазвание и аннотация: Перевод в очереди
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Hu Haoyue, Lang Minzhe, Zhao Mingming, Li Wenwen, Zhi Shaoce, Liu Peng, Hong Guangliang
Аннотация
OBJECTIVE: This study aims to develop a diagnostic model capable of accurately distinguishing between TBM and non-TBM in adults using basic clinical and laboratory parameters.
METHODS: Demographic information, clinical symptoms, imaging characteristics, and cerebrospinal fluid (CSF) parameters were collected. Consecutive patients diagnosed with TBM or non-TBM who met the inclusion and exclusion criteria were enrolled from June 2019 to June 2024 at Ningbo No.2 Hospital (training set). A random sample of 100 eligible patients from the same period at The First Affiliated Hospital of Wenzhou Medical University served as the validation set. LASSO regression with 10-fold cross-validation was applied for variable selection, generating a set of 53 variables with non-zero coefficients. These variables were then ranked by the magnitude of their coefficients, and the top 7 variables with the highest importance were retained for the final multivariate logistic regression model. Model performance, including discrimination, calibration, and clinical utility, was validated in both cohorts.
RESULTS: Multivariate logistic regression identified seizures at admission, mental and behavioral abnormalities, thalamic abnormalities on brain MRI, a positive Pandy test, cerebrospinal fluid (CSF) pressure, CSF glucose levels, and serum albumin as independent predictors of TBM (p < 0.05). The model showed promising discrimination (AUC 0.876 vs. 0.958) and good calibration in both cohorts. Decision curve analysis confirmed its clinical utility, suggesting potential to enhance diagnostic practice.
CONCLUSION: This multivariable diagnostic model can assist clinicians in early differentiation between TBM and non-TBM using simple clinical and laboratory parameters.
METHODS: Demographic information, clinical symptoms, imaging characteristics, and cerebrospinal fluid (CSF) parameters were collected. Consecutive patients diagnosed with TBM or non-TBM who met the inclusion and exclusion criteria were enrolled from June 2019 to June 2024 at Ningbo No.2 Hospital (training set). A random sample of 100 eligible patients from the same period at The First Affiliated Hospital of Wenzhou Medical University served as the validation set. LASSO regression with 10-fold cross-validation was applied for variable selection, generating a set of 53 variables with non-zero coefficients. These variables were then ranked by the magnitude of their coefficients, and the top 7 variables with the highest importance were retained for the final multivariate logistic regression model. Model performance, including discrimination, calibration, and clinical utility, was validated in both cohorts.
RESULTS: Multivariate logistic regression identified seizures at admission, mental and behavioral abnormalities, thalamic abnormalities on brain MRI, a positive Pandy test, cerebrospinal fluid (CSF) pressure, CSF glucose levels, and serum albumin as independent predictors of TBM (p < 0.05). The model showed promising discrimination (AUC 0.876 vs. 0.958) and good calibration in both cohorts. Decision curve analysis confirmed its clinical utility, suggesting potential to enhance diagnostic practice.
CONCLUSION: This multivariable diagnostic model can assist clinicians in early differentiation between TBM and non-TBM using simple clinical and laboratory parameters.