Dynamical modeling of hospital transmission and antibiotic resistance evolution in a multidrug resistant nosocomial pathogen

多重耐药医院病原体的医院传播和抗生素耐药性进化的动态模型

• 批准号: 10089393
• 负责人: Robert J Woods
• 金额: $ 62.39万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10089393
• 关键词: Accounting; Admission activity; Antibiotic Resistance; Antibiotic-resistant organism; Antibiotics; Bacteria; Bioinformatics; Biological Assay; Clinical; Collection; Communities; Computerized Medical Record; Custom; Daptomycin; Data; Enterococcus faecium; Epidemiology; Evolution; General Hospitals; Genetic; Goals; Hospitals; Infection prevention; Intervention; Knowledge; Linezolid; Location; Markov Chains; Medical Records; Methods; Microbiology; Modeling; Modern Medicine; Movement; Multi-Drug Resistance; Nosocomial Infections; Organism; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Phylogenetic Analysis; Prevention Measures; Prevention strategy; Process; Protocols documentation; Recovery; Refractory; Resistance; Risk Factors; Role; Sampling; Signal Transduction; Source; Structure; System; Techniques; Testing; Work; base; data integration; design; electronic data; epidemiological model; genetic analysis; improved; novel; pathogen; portability; predictive modeling; surveillance data; theories; therapy design; tool; transmission process; trend; whole genome

PROJECT SUMMARY/ABSTRACT Enterococcus faecium is a leading cause of hospital acquired infections that has proven refractory to infection prevention measures and has evolved increasing levels of antibiotic resistance over the last 40 years. How resistance evolves and spreads in this pathogen is uncertain because transmission and selection are hidden processes: transmission occurs silently between asymptomatically colonized patients, which obscures the signal of selection observed from clinical isolates. The proposed work will develop and deploy powerful new statistical inference techniques to assimilate data from electronic medical records, microbiological samples, and whole genome sequences into explicit, mechanistic models of transmission and antibiotic resistance evolution in E. faecium. The work is made possible by unique features of the study system: we have documented ongoing transmission and resis- tance evolution in the pathogen E. faecium and possess both a nearly perfect record of patient movement and antibiotic exposure and a large collection of patient samples from a thorough and active surveillance protocol. The specific aims of the proposal are: (I) To develop and fit a detailed E. faecium transmission model to medical record data to precisely quantify: (i) transmission rates, (ii) recovery rates, (iii) the rate of evo- lution of resistance, (iv) drivers of these rates, including contact precautions and antibiotic exposure, and (v) potential interactions between resistance and transmissibility. (II) Bioinformatic approaches that utilize whole genome sequences for c. 600 E. faecium isolates/yr and electronic medical records will be used to es- timate size, structure, and location of transmission chains and characterize patterns of resistance evolution across the resulting transmission network. (III) Hypotheses based on the transition model from Aim I will be directly tested by using the genetic data from Aim II. The methods developed herein will be applicable to a broad array of pathogens and clinical settings, and will facilitate the rational design of strategies to slow or even reverse the evolution of antibiotic resistance. In particular, the models and protocols will be portable to hospitals generally, where they will be useful for designing interventions.

项目总结/摘要 屎肠球菌是医院获得性感染的主要原因，已被证明是难治性感染 在过去的40年里，抗生素耐药性的水平不断提高。如何 由于传播和选择是隐藏的，因此这种病原体的抗性进化和传播是不确定的。 过程：传播在无症状定植患者之间悄无声息地发生， 从临床分离株中观察到的选择信号。 拟议的工作将开发和部署强大的新的统计推断技术， 数据从电子病历，微生物样本，和全基因组序列到明确的， E.屎室工作是做出来的 研究系统的独特功能可能：我们已经记录了正在进行的传播和抵抗， 病原菌E.粪便，并拥有一个近乎完美的记录，病人的运动 和抗生素暴露以及从彻底和积极的监测中收集大量患者样本 议定书 该建议的具体目标是：（一）制定和拟合一个详细的E。粪便传播模型， 医疗记录数据，以精确量化：（一）传输率，（二）恢复率，（三）evo- 耐药性的消除，（iv）这些比率的驱动因素，包括接触预防措施和抗生素暴露，以及 (v)抗性和遗传性之间的潜在相互作用。(II)生物信息学方法利用 c.全基因组序列600 E.粪便分离株/年和电子病历将用于评估- 估计传播链的大小、结构和位置，并描述耐药性演变的模式 在所产生的传输网络中。(III)基于Aim I过渡模型的假设将 使用Aim II的遗传数据直接进行测试。 本文开发的方法将适用于广泛的病原体和临床环境， 将有助于合理设计策略，减缓甚至逆转抗生素耐药性的演变。 特别是，模型和协议通常可移植到医院，在那里它们将用于 设计干预措施。