Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis
BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between Janua...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:6cd6e5420f1748978a8cd792a6fbf69b2021-11-12T04:49:23ZDiagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis1664-322410.3389/fimmu.2021.731876https://doaj.org/article/6cd6e5420f1748978a8cd792a6fbf69b2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fimmu.2021.731876/fullhttps://doaj.org/toc/1664-3224BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.ResultsA total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840–0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%–77.77%) and a specificity of 92.86% (95% CI, 85.98%–96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921–0.978), with 81.58% (95% CI, 71.42%–88.70%) sensitivity and 91.84% (95% CI, 84.71%–95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867–0.980) with 79.49% (95% CI, 64.47%–89.22%) sensitivity and 90.91% (95% CI, 81.55%–95.77%) specificity.ConclusionsThe diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.Ying LuoYing XueQun LinQun LinLiyan MaoGuoxing TangHuijuan SongWei LiuShiji WuWeiyong LiuYu ZhouLingqing XuZhigang XiongTing WangXu YuanYong GanZiyong SunFeng WangFrontiers Media S.A.articletuberculous meningitisbacterial meningitisdifferential diagnosisTBAg/PHA ratiodiagnostic modelImmunologic diseases. AllergyRC581-607ENFrontiers in Immunology, Vol 12 (2021) |
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EN |
| topic |
tuberculous meningitis bacterial meningitis differential diagnosis TBAg/PHA ratio diagnostic model Immunologic diseases. Allergy RC581-607 |
| spellingShingle |
tuberculous meningitis bacterial meningitis differential diagnosis TBAg/PHA ratio diagnostic model Immunologic diseases. Allergy RC581-607 Ying Luo Ying Xue Qun Lin Qun Lin Liyan Mao Guoxing Tang Huijuan Song Wei Liu Shiji Wu Weiyong Liu Yu Zhou Lingqing Xu Zhigang Xiong Ting Wang Xu Yuan Yong Gan Ziyong Sun Feng Wang Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis |
| description |
BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.ResultsA total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840–0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%–77.77%) and a specificity of 92.86% (95% CI, 85.98%–96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921–0.978), with 81.58% (95% CI, 71.42%–88.70%) sensitivity and 91.84% (95% CI, 84.71%–95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867–0.980) with 79.49% (95% CI, 64.47%–89.22%) sensitivity and 90.91% (95% CI, 81.55%–95.77%) specificity.ConclusionsThe diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM. |
| format |
article |
| author |
Ying Luo Ying Xue Qun Lin Qun Lin Liyan Mao Guoxing Tang Huijuan Song Wei Liu Shiji Wu Weiyong Liu Yu Zhou Lingqing Xu Zhigang Xiong Ting Wang Xu Yuan Yong Gan Ziyong Sun Feng Wang |
| author_facet |
Ying Luo Ying Xue Qun Lin Qun Lin Liyan Mao Guoxing Tang Huijuan Song Wei Liu Shiji Wu Weiyong Liu Yu Zhou Lingqing Xu Zhigang Xiong Ting Wang Xu Yuan Yong Gan Ziyong Sun Feng Wang |
| author_sort |
Ying Luo |
| title |
Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis |
| title_short |
Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis |
| title_full |
Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis |
| title_fullStr |
Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis |
| title_full_unstemmed |
Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis |
| title_sort |
diagnostic model for discrimination between tuberculous meningitis and bacterial meningitis |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/6cd6e5420f1748978a8cd792a6fbf69b |
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