结核性脑膜炎与细菌性脑膜炎鉴别诊断模型。
Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis.
机构信息
Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
出版信息
Front Immunol. 2021 Nov 12;12:731876. doi: 10.3389/fimmu.2021.731876. eCollection 2021.
BACKGROUND
The 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.
METHODS
Patients 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.
RESULTS
A 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.
CONCLUSIONS
The diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.
背景
结核性脑膜炎（TBM）与细菌性脑膜炎（BM）的鉴别诊断在临床实践中仍然具有挑战性。本研究旨在建立一种能够准确区分 TBM 和 BM 的诊断模型。
方法
2017 年 1 月至 2021 年 1 月，在同济医院（硚口队列）和中法新城医院（蔡甸队列）招募 TBM 或 BM 患者。同时进行脑脊液（CSF）检测指标和 T-SPOT 检测。采用多变量逻辑回归建立诊断模型。
结果
硚口队列和蔡甸队列分别纳入了 174 例患者（76 例 TBM 和 98 例 BM）和 105 例患者（39 例 TBM 和 66 例 BM）。与 BM 组相比，TBM 组的 CSF 淋巴细胞比例明显升高，而 CSF 氯、有核细胞计数和中性粒细胞比例明显降低。然而，受试者工作特征（ROC）曲线分析显示，这些指标的 ROC 曲线下面积（AUCs）均低于 0.8。同时，结核特异性抗原/植物血凝素（TBAg/PHA）比值在鉴别 TBM 和 BM 方面的 AUC 为 0.889（95%CI，0.840-0.938），当截断值为 0.163 时，敏感性为 68.42%（95%CI，57.30%-77.77%），特异性为 92.86%（95%CI，85.98%-96.50%）。因此，我们成功地建立了一个基于 TBAg/PHA 比值、CSF 氯、CSF 有核细胞计数和 CSF 淋巴细胞比例组合的诊断模型，用于区分 TBM 和 BM。该模型在区分 TBM 和 BM 方面表现出良好的性能（AUC：0.949；95%CI，0.921-0.978），具有 81.58%（95%CI，71.42%-88.70%）的敏感性和 91.84%（95%CI，84.71%-95.81%）的特异性。在蔡甸队列中进一步验证了硚口队列中获得的诊断模型的性能。该诊断模型在蔡甸队列中的 AUC 为 0.923（95%CI，0.867-0.980），敏感性为 79.49%（95%CI，64.47%-89.22%），特异性为 90.91%（95%CI，81.55%-95.77%）。
结论
基于四个指标组合建立的诊断模型在区分 TBM 和 BM 方面具有优异的应用价值。
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