Al-Sultani Zahraa, Inglis Timothy Jj, McFadden Benjamin, Thomas Elizabeth, Reynolds Mark
School of Physics, Maths and Computing, Computer Science and Software Engineering, University of Western Australia, Crawley, WA 6009, Australia.
Division of Pathology and Laboratory Medicine, School of Medicine, University of Western Australia, Crawley, WA 6009, Australia.
J Med Microbiol. 2025 Mar;74(3). doi: 10.1099/jmm.0.001986.
Repurposing electronic health record (EHR) or electronic medical record (EMR) data holds significant promise for evidence-based epidemic intelligence and research. Key challenges include sepsis recognition by physicians and issues with EHR and EMR data. Recent advances in data-driven techniques, alongside initiatives like the Surviving Sepsis Campaign and the Severe Sepsis and Septic Shock Management Bundle (SEP-1), have improved sepsis definition, early detection, subtype characterization, prognostication and personalized treatment. This includes identifying potential biomarkers or digital signatures to enhance diagnosis, guide therapy and optimize clinical management. Machine learning applications play a crucial role in identifying biomarkers and digital signatures associated with sepsis and its sub-phenotypes. Additionally, electronic phenotyping, leveraging EHR and EMR data, has emerged as a valuable tool for evidence-based sepsis identification and management. This review examines methods for identifying sepsis cohorts, focusing on two main approaches: utilizing health administrative data with standardized diagnostic coding via the International Classification of Diseases and integrating clinical data. This overview provides a comprehensive analysis of current cohort identification and electronic phenotyping strategies for sepsis, highlighting their potential applications and challenges. The accuracy of an electronic phenotype or signature is pivotal for precision medicine, enabling a shift from subjective clinical descriptions to data-driven insights.
重新利用电子健康记录（EHR）或电子病历（EMR）数据在基于证据的流行病情报和研究方面具有重大前景。关键挑战包括医生对脓毒症的识别以及EHR和EMR数据的问题。数据驱动技术的最新进展，以及诸如拯救脓毒症运动和严重脓毒症及脓毒性休克管理集束方案（SEP-1）等举措，改善了脓毒症的定义、早期检测、亚型特征描述、预后评估和个性化治疗。这包括识别潜在的生物标志物或数字特征，以加强诊断、指导治疗并优化临床管理。机器学习应用在识别与脓毒症及其亚表型相关的生物标志物和数字特征方面发挥着关键作用。此外，利用EHR和EMR数据的电子表型分析已成为基于证据的脓毒症识别和管理的宝贵工具。本综述探讨了识别脓毒症队列的方法，重点关注两种主要方法：通过国际疾病分类利用带有标准化诊断编码的卫生行政数据以及整合临床数据。本概述对当前脓毒症队列识别和电子表型分析策略进行了全面分析，突出了它们的潜在应用和挑战。电子表型或特征的准确性对于精准医学至关重要，能够实现从主观临床描述向数据驱动见解的转变。
