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Rapid, Breath Volatile Metabolite-Based Diagnostic for In Vivo Identification and Antibiotic Resistance Profiling of Bacterial Pathogens in Ventilator-Associated Pneumonia

基于呼吸挥发性代谢物的快速诊断，用于呼吸机相关肺炎细菌病原体的体内鉴定和抗生素耐药性分析

基本信息

批准号：
10630048
负责人：
Sophia Koo
金额：
$ 110.69万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-20 至 2024-05-31
项目状态：
已结题

项目摘要

Project Summary/Abstract: The lack of diagnostics that rapidly and accurately identify bacterial infections drives empiric antibiotic prescribing in patients with pneumonia – ultimately, 37-50% of these antibiotics are unnecessary. These issues are amplified in the intensive care unit (ICU), where antimicrobial resistance is common, the risk of imminent clinical deterioration and death is high, and clinicians are under pressure to make rapid treatment decisions. Ventilator-associated pneumonia (VAP) is the most common ICU hospital-acquired infection, responsible for approximately half of all ICU antibiotic prescribing. Time to effective antibiotic treatment is a critical determinant of outcome, but many patients with VAP receive inadequate empiric treatment due to the high prevalence of resistant organisms in VAP. Clinical findings in VAP are highly nonspecific, and 30-60% of antibiotics prescribed for suspected VAP are ultimately unnecessary. Despite a high pulmonary bacterial load in patients with VAP, the lung has traditionally been a particularly inaccessible space without the use of invasive diagnostic procedures. We have established proof of concept in murine VAP models that there are bacterial species-specific breath volatile metabolite signatures in VAP caused by Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli and Klebsiella pneumoniae, and that microbial breath volatile metabolites have markedly different responses to antibiotic exposure within a few hours in phenotypically susceptible (S) vs. non-susceptible (NS) organisms. In close collaboration with industry partners and a team of experts in antimicrobial resistance, microbiology, VAP, advanced statistical methods, and regulatory matters, we propose further development of an advanced, miniaturized gas chromatography-differential mobility spectrometry (GC-DMS) diagnostic platform for the rapid, noninvasive, breath-based diagnosis of VAP and its most common causative pathogens, S. aureus, P. aeruginosa, K. pneumoniae, E. coli, Enterobacter cloacae, and Acinetobacter baumannii, exploiting differential volatile metabolite responses to effective and ineffective antibiotic therapy to obtain in vivo phenotypic information about antibiotic susceptibility. Using thermal desorption-GC-tandem mass spectrometry and in parallel, a rapid GC-DMS diagnostic device, we will systematically characterize these species-specific breath signatures and early responses to antibiotic therapy in S vs. NS organisms in murine VAP models and in patients with suspected VAP, defining and validating breath signatures that (a) identify VAP, distinguishing it from other ventilator-associated conditions and respiratory tract colonization, (b) identify its underlying microbial etiology, and (c) determine whether the microbe is S or NS by examining its early response to antibiotics, and create GC-DMS algorithms that identify these signatures in breath data automatically, in preparation for a 510(k) clearance study. This diagnostic device will transform the care of patients with VAP and sharply reduce diagnostic delays, both facilitating early administration of appropriate antibiotics and reducing unnecessary antibiotic use.

项目摘要/摘要：缺乏快速准确识别细菌感染的诊断方法推动了经验性抗生素的使用给肺炎患者开处方——最终，其中 37-50% 的抗生素是不必要的。这些问题在抗菌药物耐药性普遍存在的重症监护病房 (ICU) 中，这种情况被放大，迫在眉睫的风险临床恶化和死亡率很高，临床医生面临着快速做出治疗决定的压力。呼吸机相关性肺炎 (VAP) 是 ICU 最常见的医院获得性感染，导致大约一半的 ICU 抗生素处方。有效抗生素治疗的时间至关重要是结果的决定因素，但由于高风险，许多 VAP 患者接受的经验性治疗不足 VAP 中耐药微生物的流行。 VAP 的临床表现高度非特异性，30-60% 对于疑似 VAP 开抗生素治疗最终是不必要的。尽管肺部细菌载量很高对于 VAP 患者，如果不使用呼吸机，肺部传统上是一个特别难以进入的空间侵入性诊断程序。我们已经在小鼠 VAP 模型中建立了概念证明，金黄色葡萄球菌引起的 VAP 中细菌物种特异性呼吸挥发性代谢物特征，铜绿假单胞菌、大肠杆菌和肺炎克雷伯菌，以及呼吸挥发性微生物代谢物在表型上几个小时内对抗生素暴露有明显不同的反应易感 (S) 与非易感 (NS) 生物体。与行业合作伙伴和团队密切合作抗菌素耐药性、微生物学、VAP、先进统计方法和监管事务方面的专家，我们建议进一步开发先进的小型气相色谱-差动迁移率光谱分析 (GC-DMS) 诊断平台，用于快速、无创、基于呼吸的 VAP 及其相关疾病的诊断最常见的致病病原体：金黄色葡萄球菌、铜绿假单胞菌、肺炎克雷伯菌、大肠杆菌、阴沟肠杆菌、和鲍曼不动杆菌，利用不同的挥发性代谢物对有效和无效的反应抗生素治疗以获得有关抗生素敏感性的体内表型信息。使用热解吸-GC-串联质谱仪和并行的快速GC-DMS诊断装置，我们将系统地描述这些物种特异性呼吸特征和对抗生素治疗的早期反应鼠 VAP 模型和疑似 VAP 患者中的 S 与 NS 微生物，定义和验证呼吸 (a) 识别 VAP 的特征，将其与其他呼吸机相关疾病和呼吸系统疾病区分开来道定植，(b) 确定其潜在的微生物病因，以及 (c) 确定微生物是否为 S 或 NS 通过检查其对抗生素的早期反应，并创建识别这些特征的 GC-DMS 算法自动记录呼吸数据，为 510(k) 清除研究做准备。该诊断设备将转变 VAP 患者的护理并大幅减少诊断延误，两者都有助于早期施用适当使用抗生素并减少不必要的抗生素使用。