Ciofi Degli Atti Marta Luisa, Pecoraro Fabrizio, Piga Simone, Luzi Daniela, Raponi Massimiliano
Clinical Epidemiology Unit, Bambino Gesù Children's Hospital, Rome, Italy.
National Research Council, Institute for Research on Population and Social Policies, Rome, Italy.
Surg Infect (Larchmt). 2020 Oct;21(8):716-721. doi: 10.1089/sur.2019.238. Epub 2020 Feb 27.
Electronic surveillance using clinical and administrative data from multiple sources has been reported as a tool for surveillance of surgical site infections (SSIs), but experiences are limited. In this study, we aimed to assess the accuracy of a text-searching algorithm to detect SSIs in children based on the application of regular expressions of unstructured clinical notes collected through different information systems. We developed an information system data warehouse that integrates data provided by electronic health and administrative records for patients who underwent surgical procedures in index weeks when active SSIs surveillances was conducted. To capture whether the patient developed an SSI, we developed a customized application to analyze clinical notes and code descriptions applying a pattern-matching algorithm based on regular expressions. We described the SSI cases detected by the active surveillance and the text-searching algorithm. To assess the accuracy in identifying the SSIs through the two methods, we adopted a reference standard that calculated the total number of SSIs as those detected by active surveillance plus those derived by the text-searching algorithm that was missed by active surveillance. Compared with the total number of SSIs used as a reference standard, both methods had a specificity of 100%, a positive predictive value of 100%, and a negative predictive value >99.5%. Sensitivity was 70% for the text-mining algorithm and 60% for the active surveillance. Accuracy was >99% with both methods. The kappa value was 0.46. Compared with conventional surveillance of SSIs, a text-searching algorithm is a valid tool for case finding that has the potential to reduce drastically the workload of conventional surveillance, which involved direct contact with all families.
利用来自多个来源的临床和管理数据进行电子监测已被报道为一种监测手术部位感染（SSI）的工具，但相关经验有限。在本研究中，我们旨在基于通过不同信息系统收集的非结构化临床记录的正则表达式应用，评估一种文本搜索算法检测儿童SSI的准确性。我们开发了一个信息系统数据仓库，该仓库整合了在进行主动SSI监测的索引周内接受手术的患者的电子健康和管理记录所提供的数据。为了确定患者是否发生了SSI，我们开发了一个定制应用程序，以基于正则表达式的模式匹配算法分析临床记录和编码描述。我们描述了通过主动监测和文本搜索算法检测到的SSI病例。为了评估通过这两种方法识别SSI的准确性，我们采用了一种参考标准，该标准将SSI的总数计算为主动监测检测到的数量加上文本搜索算法检测到但被主动监测遗漏的数量。与用作参考标准的SSI总数相比，两种方法的特异性均为100%，阳性预测值为100%，阴性预测值>99.5%。文本挖掘算法的灵敏度为70%，主动监测的灵敏度为60%。两种方法的准确性均>99%。kappa值为0.46。与传统的SSI监测相比，文本搜索算法是一种有效的病例发现工具，有可能大幅减少传统监测的工作量，传统监测需要与所有家庭直接接触。
