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首页> 外文期刊>Journal of Infection >Syndromic surveillance of surgical site infections - A case study in coronary artery bypass graft patients
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Syndromic surveillance of surgical site infections - A case study in coronary artery bypass graft patients

机译:手术部位感染的症状监测-以冠状动脉搭桥术患者为例

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摘要

Objective: There is a wealth of data routinely collected and stored by healthcare facilities, which are not consistently exploited for surveillance of healthcare associated infections (HCAI). Syndromic surveillance has not yet been widely applied to HCAI. This study aimed to create syndromic surveillance for surgical site infections (SSI) following coronary artery bypass graft (CABG) procedures. Methods: A cohort of CABG patients from Imperial College Healthcare NHS Trust was investigated. Data from the local Patient Administration System, Laboratory Information Management System, radiology department, cardiac registry and Health Protection Agency SSI surveillance were linked. This data was explored for biological markers and proxies of infection, which were used to develop syndromic surveillance algorithms; sensitivity analysis was used to determine the best algorithms. Results: 303 patients were included, with a SSI incidence of 6.6%. Wound culture requests, raised platelet and fibrinogen levels were all found to be good indicators of SSI. Two algorithms were generated, one to detect all SSI (sensitivity: 90%; specificity: 93.8%) and one to detect organ space infections specifically (sensitivity: 100%; specificity: 98.5%). Conclusion: Data which is routinely collected and stored in healthcare facilities can be used for syndromic surveillance of SSI, allowing for an efficient surveillance system without the need for resource intensive data collection.
机译:目的:医疗机构定期收集和存储大量数据,这些数据不能一直用于监视医疗相关感染(HCAI)。症状监测尚未广泛应用于HCAI。这项研究旨在为冠状动脉搭桥术(CABG)后的手术部位感染(SSI)建立症状监测。方法:对来自帝国大学医疗保健NHS Trust的CABG患者队列进行了调查。来自本地患者管理系统,实验室信息管理系统,放射科,心脏登记系统和卫生防护局SSI监视的数据已链接。探索了该数据的生物学标记和感染代理,用于开发症状监测算法。敏感性分析用于确定最佳算法。结果:纳入303例患者,SSI发生率为6.6%。伤口培养要求,血小板和纤维蛋白原水平升高均被视为SSI的良好指标。生成了两种算法,一种用于检测所有SSI(敏感性:90%;特异性:93.8%),一种用于特异性检测器官空间感染的算法(敏感性:100%;特异性:98.5%)。结论:常规收集并存储在医疗机构中的数据可用于SSI的症状监测,从而无需资源密集型数据收集即可建立有效的监测系统。

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