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A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features
Background: The discrimination of tuberculous meningitis and bacterial meningitis remains difficult at present, even with the introduction of advanced diagnostic tools. This study aims to differentiate these two kinds of meningitis by using the rule of clinical and laboratory features. Methods: A pr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6978638/ https://www.ncbi.nlm.nih.gov/pubmed/32010636 http://dx.doi.org/10.3389/fcimb.2019.00448 |
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author | Yang, Yun Qu, Xin-Hui Zhang, Kun-Nan Wu, Xiao-Mu Wang, Xin-Rong Wen, An Li, Ling-Juan |
author_facet | Yang, Yun Qu, Xin-Hui Zhang, Kun-Nan Wu, Xiao-Mu Wang, Xin-Rong Wen, An Li, Ling-Juan |
author_sort | Yang, Yun |
collection | PubMed |
description | Background: The discrimination of tuberculous meningitis and bacterial meningitis remains difficult at present, even with the introduction of advanced diagnostic tools. This study aims to differentiate these two kinds of meningitis by using the rule of clinical and laboratory features. Methods: A prospective observational study was conducted to collect the clinical and laboratory parameters of patients with tuberculous meningitis or bacterial meningitis. Logistic regression was used to define the diagnostic formula for the discrimination of tuberculous meningitis and bacterial meningitis. A receiver operator characteristic curve was established to determine the best cutoff point for the diagnostic formula. Results: Five parameters (duration of illness, coughing for two or more weeks, meningeal signs, blood sodium, and percentage of neutrophils in cerebrospinal fluid) were predictive of tuberculous meningitis. The diagnostic formula developed from these parameters was 98% sensitive and 82% specific, while these were 95% sensitive and 91% specific when prospectively applied to another 70 patients. Conclusion: The diagnostic formula developed in the present study can help physicians to differentiate tuberculous meningitis from bacterial meningitis in high-tuberculosis-incidence-areas, particularly in settings with limited microbiological and radiological resources. |
format | Online Article Text |
id | pubmed-6978638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69786382020-02-01 A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features Yang, Yun Qu, Xin-Hui Zhang, Kun-Nan Wu, Xiao-Mu Wang, Xin-Rong Wen, An Li, Ling-Juan Front Cell Infect Microbiol Cellular and Infection Microbiology Background: The discrimination of tuberculous meningitis and bacterial meningitis remains difficult at present, even with the introduction of advanced diagnostic tools. This study aims to differentiate these two kinds of meningitis by using the rule of clinical and laboratory features. Methods: A prospective observational study was conducted to collect the clinical and laboratory parameters of patients with tuberculous meningitis or bacterial meningitis. Logistic regression was used to define the diagnostic formula for the discrimination of tuberculous meningitis and bacterial meningitis. A receiver operator characteristic curve was established to determine the best cutoff point for the diagnostic formula. Results: Five parameters (duration of illness, coughing for two or more weeks, meningeal signs, blood sodium, and percentage of neutrophils in cerebrospinal fluid) were predictive of tuberculous meningitis. The diagnostic formula developed from these parameters was 98% sensitive and 82% specific, while these were 95% sensitive and 91% specific when prospectively applied to another 70 patients. Conclusion: The diagnostic formula developed in the present study can help physicians to differentiate tuberculous meningitis from bacterial meningitis in high-tuberculosis-incidence-areas, particularly in settings with limited microbiological and radiological resources. Frontiers Media S.A. 2020-01-17 /pmc/articles/PMC6978638/ /pubmed/32010636 http://dx.doi.org/10.3389/fcimb.2019.00448 Text en Copyright © 2020 Yang, Qu, Zhang, Wu, Wang, Wen and Li. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cellular and Infection Microbiology Yang, Yun Qu, Xin-Hui Zhang, Kun-Nan Wu, Xiao-Mu Wang, Xin-Rong Wen, An Li, Ling-Juan A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features |
title | A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features |
title_full | A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features |
title_fullStr | A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features |
title_full_unstemmed | A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features |
title_short | A Diagnostic Formula for Discrimination of Tuberculous and Bacterial Meningitis Using Clinical and Laboratory Features |
title_sort | diagnostic formula for discrimination of tuberculous and bacterial meningitis using clinical and laboratory features |
topic | Cellular and Infection Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6978638/ https://www.ncbi.nlm.nih.gov/pubmed/32010636 http://dx.doi.org/10.3389/fcimb.2019.00448 |
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