Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Accurate detection of sepsis during emergency department triage would allow early initiation of lab analysis, antibiotic administration, and other sepsis treatment protocols. The purpose of this study was to determine whether EHR data can be extracted and synthesized with the latest machine learning algorithms (KATE Sepsis) and clinical natural language processing to produce accurate sepsis models, and compare KATE Sepsis performance with existing sepsis screening protocols, such as SIRS and qSOFA. A machine learning model (KATE Sepsis) was developed using patient encounters with triage data from 16 participating hospitals. KATE Sepsis, SIRS, standard screening (SIRS with source of infection) and qSOFA were tested in three settings. Cohort-A was a retrospective analysis on medical records from a single Site 1. Cohort-B was a prospective analysis of Site 1. Cohort-C was a retrospective analysis on Site 1 with 15 additional sites. Across all cohorts, KATE Sepsis demonstrates an AUC of 0.94-0.963 with 73-74.87% TPR and 3.76-7.17% FPR. Standard screening demonstrates an AUC of 0.682-0.726 with 39.39-51.19% TPR and 2.9-6.02% FPR. The qSOFA protocol demonstrates an AUC of 0.544-0.56, with 10.52-13.18% TPR and 1.22-1.68% FPR. For severe sepsis, across all cohorts, KATE Sepsis demonstrates an AUC of 0.935-0.972 with 70-82.26% TPR and 4.64-8.62% FPR. For septic shock, across all cohorts, KATE Sepsis demonstrates an AUC of 0.96-0.981 with 85.71-89.66% TPR and 4.85-8.8% FPR. SIRS, standard screening, and qSOFA demonstrate low AUC and TPR for severe sepsis and septic shock detection. KATE Sepsis provided substantially better sepsis detection performance in triage than commonly used screening protocols.
翻译:Sepsis是一种危及生命的器官机能失调症,是全世界范围内死亡和严重疾病的一个主要原因。在紧急部门分治期间准确检测出败血症,可以早期启动实验室分析、抗生素管理和其他败血症治疗规程。这项研究的目的是确定EHR数据是否可以与最新的机器学习算法(KATE 缩压)和临床自然语言处理进行提取和合成,以产生准确的败血模型,并将KATE 缩血病检测程序(SIRS和qSOFA)与现有的败血筛查程序(如SIRS和SIOFA.)进行比较。一个机器学习模型(KATE Sepsis)使用16个参加医院的三分治病数据进行研制。KATE Sepsis、SISRS(具有感染源的SIRS)和QSOFS.A对医疗记录进行了回顾性分析,对1号站点进行了前景分析,对1号站点进行了回顾分析,对1号站点进行了15个站点进行了分析。 KAT EPRside 和3-7-SOMSO 演示显示AU 和0.9-PRSU 0.9。