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

点击翻译按钮获取中文摘要

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.