Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study
Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study
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DOI：
10.1371/journal.pmed.1000238
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发表时间：
2010-02-01
期刊：
影响因子：
15.8
通讯作者：
Platt, Richard
中科院分区：
文献类型：
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作者：
Huang, Susan S.;Yokoe, Deborah S.;Platt, Richard
背景：医院获得性感染暴发的检测通常基于简单的规则，例如同一病房两周内出现三例单一病原体的新病例。这些规则通常只关注少数病原体，并且不考虑病原体的潜在流行率、比率的正常随机变化以及可能发生在单个病房之外的集群，例如与专业服务相关的集群。理想情况下，疫情检测计划应使用广泛的数据源评估多种病原体。 方法和结果：我们使用世界卫生组织 (WHO) 抗菌药物耐药性监测合作中心的 WHONET-SaTScan 实验室信息软件，对 2002 年至 2006 年入住一家拥有 750 个床位的学术医疗中心的患者的微生物学数据进行时空排列扫描统计。我们评估了患者的第一批分离株中每种潜在致病菌的种类。为了评估医院相关感染，仅包括入院后 2 天以上首次分离的病原体。每天在整个医院以及医院病房、专业服务机构中寻找聚集性病例，并使用类似的抗菌药物敏感性概况。我们评估了每年偶然发生的可能性少于一次的集群。对于耐甲氧西林金黄色葡萄球菌 (MRSA) 或耐万古霉素肠球菌 (VRE)，WHONET-SaTScan 生成的簇与感染控制计划之前识别的簇进行了比较，后者基于基于规则的标准，即同一病房两周内出现 3 次。两名医院流行病学家独立对每个集群的重要性进行了分类。从 2002 年到 2006 年，WHONET-SaTScan 发现了 59 个集群，涉及 2-27 名患者（中位数为 4）。通过抗菌素耐药性概况（41%）、病房（29%）、服务（13%）和全院评估（17%）来识别集群。 WHONET-SaTScan 快速检测到了两个先前已知的革兰氏阴性病原体簇。与基于规则的阈值相比，WHONET-SaTScan 仅将 73 个先前指定的 MRSA 簇中的 1 个和 87 个 VRE 簇中的 0 个视为统计上不太可能偶然发生的事件。 WHONET-SaTScan 发现了 6 个以前未知的 MRSA 和 4 个 VRE 簇。流行病学家认为，在 59 个检测到的集群中，超过 95% 的集群值得考虑，其中 27% 需要积极调查或干预。结论：自动统计软件识别出了未进行常规检测的医院集群。它还将许多先前识别的集群归类为由于正常随机波动而可能发生的事件。这种自动化方法有潜力提供有价值的实时指导，既可以识别未被识别的疫情，也可以防止不必要地实施干扰常规患者护理的资源密集型感染控制措施。
Background: Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources.Methods and Findings: We applied a space-time permutation scan statistic to microbiology data from patients admitted to a 750-bed academic medical center in 2002-2006, using WHONET-SaTScan laboratory information software from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. We evaluated patients' first isolates for each potential pathogenic species. In order to evaluate hospital-associated infections, only pathogens first isolated >2d after admission were included. Clusters were sought daily across the entire hospital, as well as in hospital wards, specialty services, and using similar antimicrobial susceptibility profiles. We assessed clusters that had a likelihood of occurring by chance less than once per year. For methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant enterococci (VRE), WHONET-SaTScan-generated clusters were compared to those previously identified by the Infection Control program, which were based on a rule-based criterion of three occurrences in two weeks in the same ward. Two hospital epidemiologists independently classified each cluster's importance. From 2002 to 2006, WHONET-SaTScan found 59 clusters involving 2-27 patients (median 4). Clusters were identified by antimicrobial resistance profile (41%), wards (29%), service (13%), and hospital-wide assessments (17%). WHONET-SaTScan rapidly detected the two previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention.Conclusions: Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care.