Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Even a few hours of delay in the treatment of sepsis results in increased mortality. Early 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 compare sepsis detection performance at ED triage (prior to the use of laboratory diagnostics) of the standard sepsis screening algorithm (SIRS with source of infection) and a machine learning algorithm trained on EHR triage data. A machine learning model (KATE Sepsis) was developed using patient encounters with triage data from 16participating hospitals. KATE Sepsis and standard screening were retrospectively evaluated on the adult population of 512,949 medical records. KATE Sepsis demonstrates an AUC of 0.9423 (0.9401 - 0.9441) with sensitivity of 71.09% (70.12% - 71.98%) and specificity of 94.81% (94.75% - 94.87%). Standard screening demonstrates an AUC of 0.6826 (0.6774 - 0.6878) with sensitivity of 40.8% (39.71% - 41.86%) and specificity of 95.72% (95.68% - 95.78%). The KATE Sepsis model trained to detect sepsis demonstrates 77.67% (75.78% -79.42%) sensitivity in detecting severe sepsis and 86.95% (84.2% - 88.81%) sensitivity in detecting septic shock. The standard screening protocol demonstrates 43.06% (41% - 45.87%) sensitivity in detecting severe sepsis and40% (36.55% - 43.26%) sensitivity in detecting septic shock. Future research should focus on the prospective impact of KATE Sepsis on administration of antibiotics, readmission rate, morbidity and mortality.

翻译：脓毒症是一种伴有器官功能障碍的危及生命的病症，是全球死亡和危重症的主要原因之一。即使治疗延误数小时，也会导致脓毒症死亡率增加。在急诊科分诊期间早期检测脓毒症，有助于及早启动实验室分析、抗生素给药及其他脓毒症治疗方案。本研究旨在比较急诊科分诊时（在实验室诊断应用前）标准脓毒症筛查算法（感染源伴随SIRS标准）与基于电子健康记录分诊数据训练的机器学习算法的脓毒症检测性能。利用16家参与医院的患者就诊分诊数据，开发了一个机器学习模型（KATE Sepsis）。回顾性评估了KATE Sepsis与标准筛查在512,949份成人医疗记录中的表现。KATE Sepsis的AUC为0.9423（0.9401–0.9441），灵敏度为71.09%（70.12%–71.98%），特异度为94.81%（94.75%–94.87%）。标准筛查的AUC为0.6826（0.6774–0.6878），灵敏度为40.8%（39.71%–41.86%），特异度为95.72%（95.68%–95.78%）。训练用于检测脓毒症的KATE Sepsis模型，在检测严重脓毒症时灵敏度为77.67%（75.78%–79.42%），在检测脓毒性休克时灵敏度为86.95%（84.2%–88.81%）。标准筛查方案在检测严重脓毒症时灵敏度为43.06%（41%–45.87%），在检测脓毒性休克时灵敏度为40%（36.55%–43.26%）。未来研究应聚焦于KATE Sepsis对抗生素给药、再入院率、发病率及死亡率的预期影响。