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.

项目摘要/摘要 粪肠球菌是医院获得性感染的主要原因，已被证明是对感染无效的。 预防措施，并在过去40年中演变出越来越多的抗生素耐药性。多么 这种病原菌的抗药性进化和传播是不确定的，因为传递和选择是隐藏的 传播过程：在无症状的被殖民的患者之间静默传播，这掩盖了 从临床分离株观察到的选择信号。 拟议的工作将开发和部署强大的新统计推断技术，以吸收 将电子病历、微生物样本和全基因组序列中的数据转换为显式， 粪肠球菌传播和耐药性进化的机制模型。作品完成了 可能通过研究系统的独特功能：我们记录了正在进行的传播和耐药性- 粪肠球菌和两者几乎完美地记录了患者的活动 和抗生素暴露以及从彻底和积极的监测中收集的大量患者样本 协议。 这项建议的具体目的是：(I)开发和fi一个详细的粪肠球菌传播模型，以 病历数据以精确量化：(I)传播率，(Ii)恢复率，(Iii)Evo- 消除耐药性；(4)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)抗性和传播性之间的潜在相互作用。(2)利用生物信息学方法 约600株粪肠球菌分离株/年的全基因组序列和电子病历将用于ES- 传输链的确切大小、结构和位置，并表征抗性进化的模式 通过由此产生的传输网络。(3)基于AIM一的过渡模式的假设将 通过使用AIM II的遗传数据直接进行测试。 这里开发的方法将适用于广泛的病原体和临床环境，以及 将有助于合理设计减缓甚至逆转抗生素耐药性演变的策略。 特别是，这些模型和协议将可移植到一般医院，在那里它们将用于 设计干预措施。