Zetola Nicola M, Modongo Chawangwa, Matsiri Ogopotse, Tamuhla Tsaone, Mbongwe Bontle, Matlhagela Keikantse, Sepako Enoch, Catini Alexandro, Sirugo Giorgio, Martinelli Eugenio, Paolesse Roberto, Di Natale Corrado
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, USA; School of Medicine, University of Botswana, Gaborone, Botswana; Botswana-UPenn Partnership, University of Pennsylvania, Gaborone, Botswana.
Botswana-UPenn Partnership, University of Pennsylvania, Gaborone, Botswana.
J Infect. 2017 Apr;74(4):367-376. doi: 10.1016/j.jinf.2016.12.006. Epub 2016 Dec 22.
We determined the performance of a sensor array (an electronic nose) made of 8 metalloporphyrins coated quartz microbalances sensors for the diagnosis and prognosis of pulmonary tuberculosis (TB) using exhaled breath samples.
TB cases and healthy controls were prospectively enrolled. Signals from volatile organic compounds (VOCs) in breath samples were measured at days 0, 2, 7, 14, and 30 of TB therapy and correlated with clinical and microbiological measurements.
Fifty one pulmonary TB cases and 20 healthy HIV-uninfected controls were enrolled in the study. 31 (61%) of the 51 pulmonary TB cases were coinfected with HIV. At day 0 (before TB treatment initiation) the sensitivity of our device was estimated at 94.1% (95% confidence interval [CI], 83.8-98.8%) and specificity was 90.0% (95% CI, 68.3-98.8%) for distinguishing TB cases from controls. Time-dependent changes in the breath signals were identified as time on TB treatment progressed. Time-dependent signal changes were more pronounced among HIV-uninfected patients.
The identification of VOCs' signals in breath samples using a sensor array achieved high sensitivity and specificity for the diagnosis of TB and allowed following signal changes during TB treatment.
我们使用呼出气体样本,测定了一种由8个涂有金属卟啉的石英微量天平传感器组成的传感器阵列(电子鼻)对肺结核(TB)的诊断和预后评估性能。
前瞻性纳入肺结核病例和健康对照。在结核病治疗的第0、2、7、14和30天测量呼出气体样本中挥发性有机化合物(VOCs)的信号,并与临床和微生物学测量结果相关联。
本研究纳入了51例肺结核病例和20名未感染HIV的健康对照。51例肺结核病例中有31例(61%)合并感染HIV。在第0天(开始抗结核治疗前),我们的设备区分肺结核病例与对照的灵敏度估计为94.1%(95%置信区间[CI],83.8 - 98.8%),特异性为90.0%(95% CI,68.3 - 98.8%)。随着结核病治疗时间的推移,呼出气体信号出现时间依赖性变化。在未感染HIV的患者中,时间依赖性信号变化更为明显。
使用传感器阵列识别呼出气体样本中VOCs的信号,对肺结核诊断具有高灵敏度和特异性,并能在结核病治疗期间跟踪信号变化。