Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models
Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models
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DOI：
10.1098/rsif.2007.0224
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发表时间：
2007-08-22
影响因子：
3.9
通讯作者：
McElwain, D. L. S.
中科院分区：
文献类型：
--
作者：
McBryde, E. S.;Pettitt, A. N.;McElwain, D. L. S.
背景。耐药医院病原体可在疫情聚集性或零星发生。基因分型通常用于区分流行性和散发的万古霉素耐药肠球菌（VRE）。我们将其与统计方法进行比较，以确定VRE的传播特性。方法和发现。提出了一种结构化的连续时间隐马尔可夫模型。隐藏状态是vre定殖患者的数量（包括检测到的和未检测到的）。本研究的输入是每周的点患病率数据；157周VRE患病率。我们估计了两个参数：一个用于量化VRE的交叉传播，另一个用于量化零星来源的VRE定殖水平。我们将结果与伴随的基因分型和表型分型所得结果进行了比较。我们估计89%的传播是由于病房交叉传播，11%是零星传播。基因分型发现90%的菌株具有相同的糖肽耐药基因，84%的菌株在脉冲场凝胶电泳（PFGE）上具有相同或接近相同的基因。根据模型选择标准，有一些证据表明交叉传输参数在整个研究期间发生了变化。在疫情爆发之前和疫情爆发高峰期允许传播变化的模型优于其他模型。该模型估计，交叉传播在第120周增加，在第135周后下降，与环境去污染一致。我们发现，hmm可以应用于系列流行数据，以估计医院病原体的获取特征，并区分流行病和零星获取。该模型能够估计传播参数，尽管不完善的检测生物体。该模型的结果与PFGE和糖肽耐药基因型数据进行了验证，结果非常相似。此外，hmm可以提供关于未被观察到的事件的信息，例如未被发现的殖民化。
Background. Antibiotic-resistant nosocomial pathogens can arise in epidemic clusters or sporadically. Genotyping is commonly used to distinguish epidemic from sporadic vancomycin-resistant enterococci (VRE). We compare this to a statistical method to determine the transmission characteristics of VRE.Methods and findings. A structured continuous-time hidden Markov model (HMM) was developed. The hidden states were the number of VRE-colonized patients (both detected and undetected). The input for this study was weekly point-prevalence data; 157 weeks of VRE prevalence. We estimated two parameters: one to quantify the cross-transmission of VRE and the other to quantify the level of VRE colonization from sporadic sources. We compared the results to those obtained by concomitant genotyping and phenotyping.We estimated that 89% of transmissions were due to ward cross-transmission while 11% were sporadic. Genotyping found that 90% had identical glycopeptide resistance genes and 84% were identical or nearly identical on pulsed-field gel electrophoresis (PFGE).There was some evidence, based on model selection criteria, that the cross-transmission parameter changed throughout the study period. The model that allowed for a change in transmission just prior to the outbreak and again at the peak of the outbreak was superior to other models. This model estimated that cross-transmission increased at week 120 and declined after week 135, coinciding with environmental decontamination.Significance. We found that HMMs can be applied to serial prevalence data to estimate the characteristics of acquisition of nosocomial pathogens and distinguish between epidemic and sporadic acquisition. This model was able to estimate transmission parameters despite imperfect detection of the organism. The results of this model were validated against PFGE and glycopeptide resistance genotype data and produced very similar results. Additionally, HMMs can provide information about unobserved events such as undetected colonization.