van der Meijden Siri Lise, van Boekel Anna M, van Goor Harry, Nelissen Rob Ghh, Schoones Jan W, Steyerberg Ewout W, Geerts Bart F, de Boer Mark Gj, Arbous M Sesmu
Intensive Care Unit, Leiden University Medical Center, Leiden, Netherlands.
Healthplus.ai BV, Amsterdam, Netherlands.
JMIR Med Inform. 2024 Sep 10;12:e57195. doi: 10.2196/57195.
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.
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.
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.
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.
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.
术后感染仍是医疗保健领域的一项严峻挑战，会导致高发病率、高死亡率和高昂成本。准确识别和标记术后细菌感染患者对于开发预测模型、验证生物标志物以及在临床实践中实施监测系统至关重要。
本范围综述旨在探索利用电子健康记录（EHR）数据识别术后感染患者的方法，以超越人工病历审查的参考标准。
我们在PubMed、Embase、科学网（核心合集）、考克兰图书馆和Emcare（Ovid）上执行了系统检索策略，目标是针对术后环境中各种细菌感染的预测和全自动监测（即无需人工检查）的研究。对于预测建模研究，我们评估了所使用的标记方法，将其分类为人工或自动。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，以及与人工病历审查相比全自动监测系统的性能。
我们在2003年至2023年发表的研究中确定了75种用于识别术后感染患者的不同方法和定义。人工标记是预测建模研究中的主要方法，65%（49/75）的已识别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其数据源之一。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。
目前没有证据支持仅基于结构化EHR数据对感染患者进行全自动标记和识别。未来的研究应专注于定义统一的定义，以及优先开发更具可扩展性的、使用结构化EHR数据进行感染检测的自动化方法。
