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.
Front Cell Infect Microbiol. 2021 Jun 29;11:575650. doi: 10.3389/fcimb.2021.575650. eCollection 2021.
Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging.
Between 2013 and 2019, 2,059 (1,097 ATB and 962 LTBI) and another 883 (372 ATB and 511 LTBI) participants were recruited based on positive T-SPOT.TB (T-SPOT) results from Qiaokou (training) and Caidian (validation) cohorts, respectively. Blood routine examination (BRE) was performed simultaneously. Diagnostic model was established according to multivariate logistic regression.
Significant differences were observed in all indicators of BRE and T-SPOT assay between ATB and LTBI. Diagnostic model built on BRE showed area under the curve (AUC) of 0.846 and 0.850 for discriminating ATB from LTBI in the training and validation cohorts, respectively. Meanwhile, TB-specific antigens spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in T-SPOT assay) produced lower AUC of 0.775 and 0.800 in the training and validation cohorts, respectively. The diagnostic model based on combination of BRE and T-SPOT showed an AUC of 0.909 for differentiating ATB from LTBI, with 78.03% sensitivity and 90.23% specificity when a cutoff value of 0.587 was used in the training cohort. Application of the model to the validation cohort showed similar performance. The AUC, sensitivity, and specificity were 0.910, 78.23%, and 90.02%, respectively. Furthermore, we also assessed the performance of our model in differentiating ATB from LTBI with lung lesions. Receiver operating characteristic analysis showed that the AUC of established model was 0.885, while a threshold of 0.587 yield a sensitivity of 78.03% and a specificity of 85.69%, respectively.
The diagnostic model based on combination of BRE and T-SPOT could provide a reliable differentiation between ATB and LTBI.
区分活动性结核病（ATB）和潜伏性结核感染（LTBI）仍然具有挑战性。
在 2013 年至 2019 年间，根据来自桥口（训练）和蔡甸（验证）队列的 T-SPOT.TB（T-SPOT）阳性结果，分别招募了 2059 名（1097 例 ATB 和 962 例 LTBI）和另外 883 名（372 例 ATB 和 511 例 LTBI）参与者。同时进行了血常规检查（BRE）。根据多变量逻辑回归建立诊断模型。
ATB 和 LTBI 之间的 BRE 和 T-SPOT 检测的所有指标均存在显著差异。基于 BRE 建立的诊断模型在训练和验证队列中区分 ATB 和 LTBI 的曲线下面积（AUC）分别为 0.846 和 0.850。同时，T-SPOT 检测中早期分泌抗原靶 6 和培养滤液蛋白 10 SFC 较大的结核特异性抗原斑点形成细胞（SFC）的 AUC 分别为 0.775 和 0.800。基于 BRE 和 T-SPOT 组合建立的诊断模型在区分 ATB 和 LTBI 方面的 AUC 为 0.909，当使用 0.587 的截断值时，在训练队列中的灵敏度为 78.03%，特异性为 90.23%。将模型应用于验证队列时，表现出相似的性能。AUC、灵敏度和特异性分别为 0.910、78.23%和 90.02%。此外，我们还评估了该模型在区分伴有肺部病变的 ATB 和 LTBI 方面的性能。接收者操作特征分析显示，建立模型的 AUC 为 0.885，而阈值为 0.587 时，灵敏度为 78.03%，特异性为 85.69%。
基于 BRE 和 T-SPOT 组合的诊断模型可可靠地区分 ATB 和 LTBI。
Zotero 插件
