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：缺乏迅速，准确鉴定细菌感染的诊断能力驱动经验性抗生素肺炎患者的处方 - 最终，这些抗生素中有37-50％是不必要的。这些问题在重症监护室（ICU）中被放大，抗菌素耐药性很常见，即迫在眉睫的风险临床定义和死亡很高，临床医生承受着快速治疗决策的压力。呼吸机相关肺炎（VAP）是最常见的ICU医院获得的感染，负责所有ICU抗生素处方的大约一半。有效抗生素治疗是关键的时间结果的决定因素，但是许多VAP患者由于高而接受经验治疗不足 VAP中抗性生物的患病率。 VAP中的临床发现高度非特异性，30-60％规定可疑VAP的抗生素最终是不必要的。尽管肺细菌负荷高在患有VAP的患者中，传统上，肺部是一个特别无法访问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细菌规格特异性的呼吸挥发性代谢物特征由金黄色葡萄球菌引起的VAP，铜绿假单胞菌，大肠杆菌和克雷伯氏菌肺炎，以及微生物呼吸挥发性代谢物在几个小时内对抗生素暴露的反应明显不同敏感的（S）与不感染的（NS）生物。与行业合作伙伴和一个团队密切合作抗菌素耐药性，微生物学，VAP，高级统计方法和调节事项专家，我们建议进一步开发高级，微型气相色谱分化的迁移率快速，无创，基于呼吸的VAP及其ITS的诊断及其诊断平台（GC-DMS）诊断平台最常见的病原体，金黄色葡萄球菌，铜绿假单胞菌，K。Pneumoniae，大肠杆菌，肠杆菌，和baumannii的Acinetobacter，利用差异性挥发性代谢物对有效和无效的反应抗生素疗法获得有关抗生素易感性的体内表型信息。使用热力解除GC-TANDEM质谱法，并同时使用快速的GC-DMS诊断设备，我们将系统地表征这些规格特定的呼吸签名和对抗生素治疗的早期反应 S与NS生物在鼠VAP模型和可疑VAP的患者中，定义和验证呼吸（a）识别VAP，将其与其他呼吸机相关条件区分开的签名和呼吸区域定植，（b）确定其潜在的微生物病因，（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，并创建识别这些签名的GC-DMS算法在呼吸数据中，自动准备了510（k）清除研究。此诊断设备将转换 VAP患者的护理并大大减少了诊断延迟，两者都支持早期给药适当的抗生素并减少不必要的抗生素使用。