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: 10561643
• 负责人: Robert J Woods
• 金额: $ 56.12万
• 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 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10561643
• 关键词: Accounting; Admission activity; Antibiotic Resistance; Antibiotic-resistant organism; Antibiotics; Bacteria; Bioinformatics; Biological; Biological Assay; Clinical; Collection; Communities; Computerized Medical Record; Custom; Daptomycin; Data; Drug resistance; Enterococcus faecium; Epidemiology; Evolution; General Hospitals; Genetic; Goals; Hospitals; Infection prevention; Intervention; Knowledge; Linezolid; Location; Markov Chains; Medical Records; Methods; Modeling; Modern Medicine; Movement; Multi-Drug Resistance; Nosocomial Infections; Organism; Patient-Focused Outcomes; Patients; Pattern; 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; data integration; electronic data; epidemiological model; genetic analysis; improved; novel; pathogen; portability; predictive modeling; rational design; 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.

项目概要/摘要 屎肠球菌是医院获得性感染的主要原因，已证明对感染具有抵抗力 过去 40 年来，抗生素耐药性不断增加。 由于传播和选择是隐藏的，因此该病原体的耐药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然发生，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来吸收 将电子病历、微生物样本和全基因组序列中的数据转化为明确的、 屎肠球菌传播和抗生素耐药性进化的机制模型已完成。 通过研究系统的独特功能，我们已经记录了持续的传播和抵抗力 屎肠球菌病原体的进化，并拥有近乎完美的患者运动记录 抗生素暴露以及通过彻底和积极的监测收集的大量患者样本 协议。 该提案的具体目标是： (I) 开发并拟合详细的屎肠球菌传播模型，以 医疗记录数据可精确量化：(i) 传输率，(ii) 恢复率，(iii) 进化率 (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) 耐药性和传播性之间的潜在相互作用 (II) 利用生物信息学方法。 每年 600 个粪肠球菌分离株的全基因组序列和电子病历将用于 传播链的最终尺寸、结构和位置以及耐药演化的特征模式 （三）基于Aim I will的转型模型的假设 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，并且 将有助于合理设计策略，以减缓甚至逆转抗生素耐药性的演变。 特别是，这些模型和协议通常可以移植到医院，在那里它们将有助于 设计干预措施。