背景:术后感染仍然是医疗保健领域的重要挑战,导致高发病率,死亡率,和成本。术后细菌感染患者的准确识别和标记对于开发预测模型至关重要,验证生物标志物,并在临床实践中实施监测系统。
目的:本范围审查旨在探索使用电子健康记录(EHR)数据识别术后感染患者的方法,以超越手动图表审查的参考标准。
方法:我们在PubMed,Embase,WebofScience(核心合集),Cochrane图书馆,和Emcare(Ovid),针对预测和全自动监测的目标研究(即,无需手动检查)术后设置的多种细菌感染。对于预测建模研究,我们评估了使用的标记方法,将它们分类为手动或自动。我们评估了术后感染监测和标记所需的不同类型的EHR数据,以及与手动图表审查相比,全自动监视系统的性能。
结果:我们在2003年至2023年之间发表的研究中确定了75种不同的方法和定义,用于识别术后感染的患者。手动标注是预测建模研究中的主要方法,65%(49/75)的确定方法使用结构化数据,45%(34/75)使用自由文本和临床笔记作为他们的数据源之一。应谨慎使用全自动监测系统,因为报告的阳性预测值在0.31至0.76之间。
结论:目前没有证据支持完全自动化的标记和识别感染患者仅基于结构化的EHR数据。未来的研究应该集中在定义统一的定义上,以及优先开发更具可扩展性的产品,使用结构化EHR数据进行感染检测的自动化方法。
BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice.
OBJECTIVE: This scoping
review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart
review.
METHODS: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart
review.
RESULTS: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76.
CONCLUSIONS: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.