运用时间间隔分布模型检测血流感染病例

潮水

Detecting related cases of bloodstream infections using time-interval distribution modelling
Journal of Hospital Infection
Volume 74, Issue 3, March 2010, Pages 250-257
Summary
An algorithm was designed to highlight related bloodstream infections using data from a nosocomial infection surveillance system to help local public health authorities direct specific measures towards clusters of cases. The approach was based on a two-step procedure. The first was a test to identify pathogens with an abnormal number of close cases. The second modelled, for the identified pathogens, the distribution of time intervals between successive cases as a mixture of two theoretical distributions in order to determine a threshold below which a specific investigation is required. The algorithm was applied to bloodstream infection surveillance data collected during a 10-year period (1996–2005) in an 878-bed teaching hospital (24 wards) in Lyon, France. The first step identified seven pathogens among the 18 being studied. The modelling succeeded in setting time thresholds to spot clusters of cases requiring further investigation with defined sensitivity and specificity. Setting the sensitivity level at 95%, the threshold values ranged from 24 days (Acinetobacter baumannii) to 294 days (Enterobacter cloacae); the specificity was higher than 70% (up to 97.5% for A. baumannii) except for E. cloacae (52.1%). Setting the specificity level at 95% resulted in a decrease in sensitivity except for A. baumannii (it reached nearly 100%); it fell below 50% for three pathogens: around 40% for Streptococcus pneumoniae and Enterococcus faecalis and 25% for Enterobacter cloacae. The threshold values then ranged from 8 days (S. pneumoniae) to 67 days (Streptococcus pyogenes). The approach proved promising though further refinements are needed before routine use.

Keywords: Bacteraemia; Distribution modelling; Disease clusters; Nosocomial infections; Surveillance system

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运用时间间隔分布模型检测血流感染病例

通过运用医院感染监测系统的数据设计一种算法用来突出血流感染来帮助当地公共卫生机构指导特定的方法来发现聚集病例。这种方法基于两个步骤。第一步是通过实验确定异常数量的聚集病例的病原。第二部的模型是对已经确立的病原，在后续病例中的时间间隔分布作为两个理论分布的混杂因素来确定一个临界值，如果低于它则需要进一步的调查。这个算法应用到了法国lyon市的一家具有878张床位教学医院10年（1996-2005）收集的血流感染监测数据中。

在第一部中从18种研究的病原中确定了7种。紧接着设定了时间界值通过定义的灵敏度和特异度来指出需要进一步调查的聚集性病例。设定灵敏度在95%，界值范围从24天（鲍曼不动杆菌）到294天（阴沟肠杆菌）；特异度除了阴沟肠杆菌为52.1%外略高于70%（最高是鲍曼不动杆菌97.5%）。设定特异度在95%则导致灵敏度的下降，除了鲍曼不动杆菌他几乎达到了100%；灵敏度对于三种病原菌下降50%：肺炎克雷伯菌和粪大肠杆菌在40%左右，阴沟肠杆菌在25%左右。然后将界值调整范围在8天（肺炎克雷伯菌）至67天（葡萄球菌）。这种方法证明再通过进一步的提炼修正是有希望能够常规使用的。 关键词：细菌；分布模型；疾病聚集；医院感染；监测系统