@文章{信息:doi/10.2196/16901，作者="范云舟和吴云舟，曹燕燕，邹雄静和朱俊宁，明和戴，迪和卢，林和尹，晓曦和熊丽娟"，标题="基于局域网过程数据多源监测的医疗相关感染的自动聚类检测:算法开发与验证回顾性研究”，期刊=“JMIR Med Inform”，年份=“2020”，月份=“10月”，日期=“23”，卷=“8”，数=“10”，页数=“e16901”，关键词=“医疗相关感染;集群检测;早期预警;多源监测;背景:卫生保健相关感染(HAI)的聚类检测对于早期识别HAI暴发至关重要。目的:验证基于局域网过程数据的多源监测能否有效检测HAI集群。方法:回顾性分析国内某三级医院4个独立高危单位的HAIs发生率及与感染相关的3项流程数据，即联合抗生素使用率、细菌标本检出率、细菌标本阳性率。我们利用Shewhart预警模型来检测时间序列数据的峰值。随后，我们根据过程数据设计了5种监测策略用于HAI聚类检测: (1) antibiotic utilization rate in combination only, (2) inspection rate of bacterial specimens only, (3) positive rate of bacterial specimens only, (4) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in parallel, and (5) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in series. We used the receiver operating characteristic (ROC) curve and Youden index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters. Results: The ROC curves of the 5 surveillance strategies were located above the standard line, and the area under the curve of the ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden indexes were 0.48 (95{\%} CI 0.29-0.67) at a threshold of 1.5 in the antibiotic utilization rate in combination--only strategy, 0.49 (95{\%} CI 0.45-0.53) at a threshold of 0.5 in the inspection rate of bacterial specimens--only strategy, 0.50 (95{\%} CI 0.28-0.71) at a threshold of 1.1 in the positive rate of bacterial specimens--only strategy, 0.63 (95{\%} CI 0.49-0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95{\%} CI 0.00-0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5. Conclusions: The multisource surveillance of process data in the area network is an effective method for the early detection of HAI clusters. The combination of multisource data and the threshold of the warning model are 2 important factors that influence the performance of the model. ", issn="2291-9694", doi="10.2196/16901", url="http://medinform.www.mybigtv.com/2020/10/e16901/", url="https://doi.org/10.2196/16901", url="http://www.ncbi.nlm.nih.gov/pubmed/32965228" }