VOLATILE MARKERS OF PULMONARY TUBERCULOSIS IN THE BREATH

呼吸中肺结核的挥发性标志物

• 批准号: 6970309
• 负责人: Michael Phillips
• 金额: $ 53.03万
• 依托单位: MENSSANA RESEARCH, INC.
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6970309
• 关键词: Mycobacterium tuberculosis; analytical method; biomarker; breath tests; clinical research; cost effectiveness; diagnosis design /evaluation; diagnosis quality /standard; human subject; lung disorder; mathematical model; miniature biomedical equipment; noninvasive diagnosis; organic chemicals; rapid diagnosis; respiratory disorder diagnosis; tuberculosis

DESCRIPTION (provided by applicant): Background: Pulmonary tuberculosis (TB) is a growing public health problem, but confirming the diagnosis in patients with suspected disease is often costly and time-consuming. Clinicians urgently need a screening test for active pulmonary tuberculosis that is faster, better and cheaper. An ideal screening test for pulmonary TB would be highly sensitive and specific as well as rapid, inexpensive and non-invasive. No test in current use fulfills these criteria: sputum staining for acid fast bacilli has high specificity but poor sensitivity, and culturing sputum for Mycobacteria has higher sensitivity, but it is expensive and time consuming. However, a Phase 1 pilot study provided evidence that a breath test for volatile organic compounds (VOCs) in the breath could provide a clinically useful new marker. Pattern recognition analysis of oxidative stress markers in breath identified patients with active pulmonary TB infection with 94.1% sensitivity and 95.1% specificity. Fuzzy logic analysis of breath VOCs predicted active pulmonary tuberculosis infection with sensitivity = 100% and specificity = 83.3%. Hypothesis: A test for breath VOCs combined with pattern recognition analysis or fuzzy logic analysis could provide a new screening test for pulmonary TB that is highly sensitive and specific as well as rapid, inexpensive and non-invasive. Plans to test the hypothesis: We will evaluate the breath test in a subset of patients enrolled in a large multicenter trial designed to evaluate antibody-detection assays in the detection of pulmonary TB. Results of the breath test will be compared to the "gold standard" of sputum culture for M. tuberculosis. Pattern recognition analysis and fuzzy logic analysis of breath VOCs will be employed to develop predictive models in a training set of patients. These models will be evaluated and compared in a test set of patients to determine the sensitivity, specificity and predictive value of the breath test as a marker of active pulmonary TB. Long term aims: a. Scientific: To validate the clinical findings of the Phase 1 study in a larger group of subjects. b. Technical: to develop a comparatively inexpensive miniaturized and rapid breath test suitable for point of care screening. c. Commercial: To obtain Food and Drug Administration (FDA) approval of the device and to market it. Importance of the research: A breath test for pulmonary TB could make an important clinical impact by detecting new infections rapidly at low cost, and also by monitoring the impact of therapy. Also, it could potentially reduce the mortality and morbidity caused by pulmonary TB.

描述（由申请人提供）： 背景：肺结核 (TB) 是一个日益严重的公共卫生问题，但对疑似疾病患者进行确诊往往成本高昂且耗时。临床医生迫切​​需要一种更快、更好、更便宜的活动性肺结核筛查测试。理想的肺结核筛查测试应具有高度敏感性和特异性，并且快速、廉价且非侵入性。 目前使用的测试没有满足这些标准：抗酸杆菌痰染色特异性高但敏感性差，分枝杆菌培养痰敏感性较高，但昂贵且耗时。然而，第一阶段试点研究提供的证据表明，呼气中挥发性有机化合物 (VOC) 的呼气测试可以提供临床上有用的新标记物。呼吸中氧化应激标志物的模式识别分析识别出活动性肺结核感染患者，敏感性为 94.1%，特异性为 95.1%。呼吸 VOC 的模糊逻辑分析预测活动性肺结核感染，敏感性 = 100%，特异性 = 83.3%。 假设：呼气挥发性有机化合物测试与模式识别分析或模糊逻辑分析相结合，可以为肺结核提供一种新的筛查测试，该测试具有高度敏感性和特异性，并且快速、廉价且非侵入性。 检验假设的计划：我们将评估参加一项大型多中心试验的一部分患者的呼吸测试，该试验旨在评估肺结核检测中的抗体检测分析。呼气测试的结果将与结核分枝杆菌痰培养的“金标准”进行比较。呼吸挥发性有机化合物的模式识别分析和模糊逻辑分析将用于开发一组患者训练的预测模型。这些模型将在一组患者测试中进行评估和比较，以确定呼气测试作为活动性肺结核标志物的敏感性、特异性和预测价值。 长期目标： 一个。科学性：在更大范围的受试者中验证第一阶段研究的临床结果。 b.技术：开发一种相对便宜的小型快速呼气测试，适用于护理点筛查。 c.商业：获得食品和药物管理局 (FDA) 批准该设备并将其推向市场。 研究的重要性：肺结核呼气测试可以通过以低成本快速检测新感染以及监测治疗效果来产生重要的临床影响。此外，它还可能降低肺结核引起的死亡率和发病率。