Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach
Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach
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DOI：
10.1017/ice.2019.288
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发表时间：
2019-12-01
影响因子：
4.5
通讯作者：
Zand, Martin S.
中科院分区：
文献类型：
--
作者：
Bush, Kristen;Barbosa, Hugo;Zand, Martin S.
目的：研究单位范围内艰难梭菌感染（CDI）易感性与住院患者流动性之间的关系，并将传染中心性作为CDI的新预测指标。设计：回顾性队列研究。方法：使用2年的患者电子健康记录数据为739张病床的医院（n = 72,636入院）的移动网络。计算每个医院单元（节点）的网络中心性指标，为每个单元（即边缘）之间的患者转移提供临床背景。使用逻辑回归计算每日单位范围内的CDI易感性评分，并与网络中心性指标进行比较，以确定单位CDI易感性与患者流动性之间的关系。结果如下：接近中心性是与单位易感性相关的统计学显著性测量（P <0.05），突出了在单位水平上预防CDI的患者流动性的重要性。传染中心性（CC）计算住院转移率，单位范围内的易感性CDI，目前医院CDI感染。传染中心性测量与我们的住院发病CDI病例结局具有统计学显著性（P <0.05），并且它捕获了与住院转移相关的额外传播机会。我们使用这种分析来创建易于解释的临床工具，显示这种关系以及真实的医院发病CDI的风险，这些工具可以在医院EHR系统中实现。结论：量化和可视化住院病人转移、全单元风险和当前感染的组合有助于识别有发生CDI爆发风险的医院单位，从而为临床医生和感染预防工作人员提供先进的警告和具体的位置数据，以告知预防工作。
Objective: To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion centrality as a new predictive measure of CDI. Design: Retrospective cohort study. Methods: A mobility network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Results: Closeness centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems. Conclusions: Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.