Branch-Elliman Westyn, Strymish Judith, Itani Kamal M F, Gupta Kalpana
Department of Medicine, Boston VA Healthcare System, Boston, MA; Department of Healthcare Quality, Division of Infection Control, Beth Israel Deaconess Medical Center, Boston, MA; Department of Medicine, Harvard University Medical School, Boston, MA.
Department of Medicine, Boston VA Healthcare System, Boston, MA; Department of Medicine, Harvard University Medical School, Boston, MA.
Am J Infect Control. 2014 Dec;42(12):1291-5. doi: 10.1016/j.ajic.2014.08.013. Epub 2014 Nov 25.
Surgical site infections (SSIs) are a common and expensive health care-associated infection, and are used as a health care quality benchmark. As such, SSI detection is a major focus of infection prevention programs. In an effort to improve on conventional surveillance methods, a simple algorithm for SSI detection was developed using clinical variables not traditionally included in National Healthcare Safety Network definitions.
A case-control study was conducted among surgeries performed at the Veterans Affairs Boston Healthcare System between January 2008 and December 2009. SSI cases were matched to controls without SSI. Clinical variables (administrative, microbiological, pharmacy, radiology) were compared between the groups to determine those that best identified SSI.
A total of 70 SSIs were matched to 70 controls. On multivariable analysis, variables significantly associated with SSI identification were wound culture order, computed tomography scan/magnetic resonance imaging order, antibiotic order within 30 days after surgery, and application of a relevant International Classification of Disease, Ninth Revision code. Among patients with no SSI identifiers, 98% were correctly classified as having no SSI. Among patients with multiple SSI identifiers, 97.1% were correctly identified as having SSI. The area under the curve for this model was 0.87.
We have derived a novel surveillance algorithm for SSI detection with excellent operating characteristics. This algorithm could be automated to streamline infection control efforts.
手术部位感染（SSIs）是一种常见且成本高昂的医疗保健相关感染，被用作医疗保健质量的基准。因此，SSI检测是感染预防计划的主要重点。为了改进传统的监测方法，利用国家医疗安全网络定义中未传统纳入的临床变量开发了一种简单的SSI检测算法。
在2008年1月至2009年12月期间于波士顿退伍军人事务医疗保健系统进行的手术中开展了一项病例对照研究。将SSI病例与无SSI的对照进行匹配。比较两组之间的临床变量（管理、微生物学、药学、放射学），以确定最能识别SSI的变量。
共将70例SSI与70例对照进行匹配。在多变量分析中，与SSI识别显著相关的变量为伤口培养医嘱、计算机断层扫描/磁共振成像医嘱、术后30天内的抗生素医嘱以及应用相关的《国际疾病分类》第九版编码。在无SSI标识符的患者中，98%被正确分类为无SSI。在有多个SSI标识符的患者中，97.1%被正确识别为有SSI。该模型的曲线下面积为0.87。
我们推导出了一种用于SSI检测的新型监测算法，具有出色的操作特性。该算法可实现自动化，以简化感染控制工作。
