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

项目概要/摘要： 由于缺乏快速准确地识别细菌感染的诊断方法， 在肺炎患者的处方-最终，37 - 50%的这些抗生素是不必要的。这些问题 在重症监护室（ICU）中被放大，在那里抗生素耐药性是常见的， 临床恶化和死亡率很高，临床医生面临着迅速做出治疗决定的压力。 呼吸机相关性肺炎（VAP）是最常见的ICU医院获得性感染， 大约一半的ICU抗生素处方。有效抗生素治疗的时间是关键 结果的决定因素，但许多VAP患者接受不充分的经验性治疗，由于高 VAP中耐药微生物的流行率。VAP的临床表现是高度非特异性的，30 - 60%的 为疑似VAP患者开具的抗生素最终没有必要。尽管肺部细菌负荷很高 在患有VAP的患者中，肺传统上是特别难以接近的空间， 侵入性诊断程序。我们已经在鼠VAP模型中建立了概念证明， 由金黄色葡萄球菌引起的VAP中的细菌物种特异性呼吸挥发性代谢物特征， 铜绿假单胞菌、大肠埃希菌和肺炎克雷伯菌，以及呼吸道挥发性微生物 代谢物在表型上对抗生素暴露在几个小时内有明显不同的反应。 敏感（S）与非敏感（NS）微生物。通过与行业合作伙伴和 抗菌素耐药性、微生物学、VAP、高级统计方法和监管事项方面的专家， 我们建议进一步发展一种先进的、小型化的气相色谱-差示迁移率 用于快速、无创、基于呼吸的VAP诊断的GC-DMS诊断平台及其 最常见的病原体，S. aureus、铜绿假单胞菌、K. pneumoniae、E.大肠杆菌，阴沟肠杆菌， 和鲍曼不动杆菌，利用有效和无效的差异挥发性代谢物反应， 抗生素治疗以获得关于抗生素敏感性的体内表型信息。使用热 解吸-气相色谱-串联质谱和并行，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 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， 道定殖，（B）鉴定其潜在的微生物病原学，和（c）确定微生物是S还是 NS通过检查其对抗生素的早期反应，并创建GC-DMS算法来识别这些特征 在呼吸数据中自动进行，为510（k）清除研究做准备。这个诊断设备将把 VAP患者的护理和大大减少诊断延迟，都有利于早期管理， 适当使用抗生素，减少不必要的抗生素使用。

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.