This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Ru CHEN
Central South University Third Xiangya Hospital
Corresponding Author
Ding Liu
Central South University Third Xiangya Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
The absence of a sufficiently accurate and efficient diagnosis of tuberculous meningitis (TBM) is major obstacle to delayed treatment, and its non-specific clinical manifestations easily mimic the central nervous system infections caused by other causes, including virus, bacteria, and cryptococcus. This study aims to develop and validate a diagnostic score system for TBM in HIV-uninfected adults by simultaneously comparing TBM with viral meningitis (VM), bacterial meningitis (BM), and cryptococcal meningitis (CM).
Methods
Twenty-nine factors (including clinical, laboratory and imaging) were assessed among 382 patients who satisfied inclusion criteria for TBM (n = 113), VM (n = 143), BM (n = 65) and CM (n = 61). Independent predictors for the diagnosis of TBM were obtained by logistic regression to establish a diagnostic scoring system. The performance of this scoring system was evaluated using a prospective validation cohort.
Results
Nine factors independently associated with the diagnosis of TBM: symptom duration (10–30 days), systemic symptoms, evidence of extra-central nervous system tuberculosis, cerebrospinal fluid (CSF) leukocyte count (100-500∗106 /mL), CSF neutrophil proportion (20%-75%), CSF protein (> 1 g/L), low serum sodium (< 137 mmol/L), meningeal enhancement, and brain parenchymal nodules (tuberculomas). The CSF neutrophil proportion was assigned a score of 2 and all other factors were assigned a score of 1. A score of at least five was suggestive of TBM with a sensitivity of 85.8% and a specificity of 87.7%, and the area under the receiver operating characteristic curve (AUC) was 0.927. When applied prospectively to an additional 72 patients (21 with TBM, 27 with VM, 14 with BM, and 10 with CM), the sensitivity, specificity, accuracy, and AUC values of this scoring model were 90.5%, 86.3%, 87.5%, and 0.944, respectively.
Conclusions
For differential diagnosis between TBM and other causes of meningitis (VM,CM and BM), we developed and validated a new weighted scoring system. The application of this scoring system can help diagnose TBM more efficiently in the early stage.
Keywords
Central nervous system infections, Tuberculous meningitis, Diagnostic scoring system, HIV negative adults
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Ru CHEN
Central South University Third Xiangya Hospital
Corresponding Author
Ding Liu
Central South University Third Xiangya Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at Diagnostic Microbiology and Infectious Disease.
Version 1
Posted 02 Jun, 2020
Community comments: 1
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Infectious Diseases
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Diagnostic Microbiology and Infectious Disease.
Background
The absence of a sufficiently accurate and efficient diagnosis of tuberculous meningitis (TBM) is major obstacle to delayed treatment, and its non-specific clinical manifestations easily mimic the central nervous system infections caused by other causes, including virus, bacteria, and cryptococcus. This study aims to develop and validate a diagnostic score system for TBM in HIV-uninfected adults by simultaneously comparing TBM with viral meningitis (VM), bacterial meningitis (BM), and cryptococcal meningitis (CM).
Methods
Twenty-nine factors (including clinical, laboratory and imaging) were assessed among 382 patients who satisfied inclusion criteria for TBM (n = 113), VM (n = 143), BM (n = 65) and CM (n = 61). Independent predictors for the diagnosis of TBM were obtained by logistic regression to establish a diagnostic scoring system. The performance of this scoring system was evaluated using a prospective validation cohort.
Results
Nine factors independently associated with the diagnosis of TBM: symptom duration (10–30 days), systemic symptoms, evidence of extra-central nervous system tuberculosis, cerebrospinal fluid (CSF) leukocyte count (100-500∗106 /mL), CSF neutrophil proportion (20%-75%), CSF protein (> 1 g/L), low serum sodium (< 137 mmol/L), meningeal enhancement, and brain parenchymal nodules (tuberculomas). The CSF neutrophil proportion was assigned a score of 2 and all other factors were assigned a score of 1. A score of at least five was suggestive of TBM with a sensitivity of 85.8% and a specificity of 87.7%, and the area under the receiver operating characteristic curve (AUC) was 0.927. When applied prospectively to an additional 72 patients (21 with TBM, 27 with VM, 14 with BM, and 10 with CM), the sensitivity, specificity, accuracy, and AUC values of this scoring model were 90.5%, 86.3%, 87.5%, and 0.944, respectively.
Conclusions
For differential diagnosis between TBM and other causes of meningitis (VM,CM and BM), we developed and validated a new weighted scoring system. The application of this scoring system can help diagnose TBM more efficiently in the early stage.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
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