Brandt Bristol N, Gartner Amanda B, Moncure Michael, Cannon Chad M, Carlton Elizabeth, Cleek Carol, Wittkopp Chris, Simpson Steven Q
University of Kansas School of Medicine, Kansas City, KS.
The University of Kansas Hospital, Kansas City, KS.
Am J Med Qual. 2015 Nov-Dec;30(6):559-65. doi: 10.1177/1062860614541291. Epub 2014 Jun 26.
An electronic sepsis surveillance system (ESSV) was developed to identify severe sepsis and determine its time of onset. ESSV sensitivity and specificity were evaluated during an 11-day prospective pilot and a 30-day retrospective trial. ESSV diagnostic alerts were compared with care team diagnoses and with administrative records, using expert adjudication as the standard for comparison. ESSV was 100% sensitive for detecting severe sepsis but only 62.0% specific. During the pilot, the software identified 477 patients, compared with 18 by adjudication. In the 30-day trial, adjudication identified 164 severe sepsis patients, whereas ESSV detected 996. ESSV was more sensitive but less specific than care team or administrative data. ESSV-identified time of severe sepsis onset was a median of 0.00 hours later than adjudication (interquartile range = 0.05). The system can be a useful tool when implemented appropriately but lacks specificity, largely because of its reliance on discreet data fields.
开发了一种电子脓毒症监测系统（ESSV），用于识别严重脓毒症并确定其发病时间。在为期11天的前瞻性试点和为期30天的回顾性试验中对ESSV的敏感性和特异性进行了评估。将ESSV诊断警报与护理团队诊断以及行政记录进行比较，以专家判定作为比较标准。ESSV检测严重脓毒症的敏感性为100%，但特异性仅为62.0%。在试点期间，该软件识别出477例患者，而经判定为18例。在为期30天的试验中，判定识别出164例严重脓毒症患者，而ESSV检测到996例。ESSV比护理团队或行政数据更敏感但特异性更低。ESSV识别的严重脓毒症发病时间比判定晚的中位数为0.00小时（四分位间距 = 0.05）。该系统在适当实施时可以成为一种有用的工具，但缺乏特异性，这主要是因为它依赖于离散的数据字段。
