Li Kui, Liu Sheng-Xi, Yang Cai-Yong, Jiang Zi-Cheng, Liu Jun, Fan Chuan-Qi, Li Tao, Dong Xue-Min, Wang Jing, Ran Ren-Yu
1 Department of Infectious Diseases, Ankang Central Hospital, Shaanxi, China.
2 The Sixth Clinical Medical School of Hubei University of Medicine, Hubei, China.
J Int Med Res. 2019 Jul;47(7):2993-3007. doi: 10.1177/0300060519851673. Epub 2019 Jun 3.
This study aimed to use the results of routine blood tests and relevant parameters to construct models for the prediction of active tuberculosis (ATB) and drug-resistant tuberculosis (DRTB) and to assess the diagnostic values of these models.
We performed logistic regression analysis to generate models of plateletcrit-albumin scoring (PAS) and platelet distribution width-treatment-sputum scoring (PTS). Area under the curve (AUC) analysis was used to analyze the diagnostic values of these curves. Finally, we performed model validation and application assessment.
In the training cohort, for the PAS model, the AUC for diagnosing ATB was 0.902, sensitivity was 82.75%, specificity was 82.20%, accuracy rate was 81.00%, and optimal threshold value was 0.199. For the PTS model, the AUC for diagnosing DRTB was 0.700, sensitivity was 63.64%, specificity was 73.53%, accuracy rate was 89.00%, and optimal threshold value was −2.202. These two models showed significant differences in the AUC analysis, compared with single-factor models. Results in the validation cohort were similar.
The PAS model had high sensitivity and specificity for the diagnosis of ATB, and the PTS model had strong predictive potential for the diagnosis of DRTB.
本研究旨在利用常规血液检查结果及相关参数构建预测活动性肺结核（ATB）和耐多药肺结核（DRTB）的模型，并评估这些模型的诊断价值。
我们进行逻辑回归分析以生成血小板压积 - 白蛋白评分（PAS）模型和血小板分布宽度 - 治疗 - 痰液评分（PTS）模型。采用曲线下面积（AUC）分析来评估这些曲线的诊断价值。最后，我们进行了模型验证和应用评估。
在训练队列中，对于PAS模型，诊断ATB的AUC为0.902，灵敏度为82.75%，特异度为82.20%，准确率为81.00%，最佳阈值为0.199。对于PTS模型，诊断DRTB的AUC为0.700，灵敏度为63.64%，特异度为73.53%，准确率为89.00%，最佳阈值为 -2.202。与单因素模型相比，这两个模型在AUC分析中显示出显著差异。验证队列中的结果相似。
PAS模型对ATB诊断具有高灵敏度和特异度，PTS模型对DRTB诊断具有较强的预测潜力。
