一项横断面研究：一种基于呼吸组学的肺结核检测方法。
A cross-sectional study: a breathomics based pulmonary tuberculosis detection method.
机构信息
Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China.
出版信息
BMC Infect Dis. 2023 Mar 10;23(1):148. doi: 10.1186/s12879-023-08112-3.
BACKGROUND
Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection.
METHOD
Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients.
RESULTS
The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961.
CONCLUSIONS
The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.
背景
肺结核（PTB）的诊断通常不准确、昂贵或复杂。基于呼吸组学的方法可能是一种快速、非侵入性的 PTB 检测的有吸引力的选择。
方法
从 518 例 PTB 患者和 887 例对照中采集呼气样本，并在实时高压光离子化飞行时间质谱仪上进行测试。采用机器学习算法进行呼吸组学分析和 PTB 检测模式，在 430 例盲法临床患者中评估其性能。
结果
在盲法测试集（n=430）中，基于呼吸组学的 PTB 检测模型的准确率为 92.6%，灵敏度为 91.7%，特异性为 93.0%，AUC 为 0.975。年龄、性别和抗结核治疗对 PTB 检测性能无显著影响。在区分 PTB 与其他肺部疾病（n=182）时，VOC 模式也具有良好的性能，准确率为 91.2%，灵敏度为 91.7%，特异性为 88.0%，AUC 为 0.961。
结论
该简单、非侵入性的基于呼吸组学的 PTB 检测方法具有较高的灵敏度和特异性，可能对临床 PTB 筛查和诊断有重要价值。
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