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首页> 外文期刊>BMJ quality & safety >Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
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Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data

机译：基于SEPSIS-3对综合医生临床标准的自动化败血症监测验证在一般医院人口中的医生记录审查：使用电子健康记录数据的观察研究

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Background Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards. Methods A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by>2 points) and the likelihood of infection by physician medical record review. Results In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards. Conclusions A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.
机译：背景技术脓毒症发病率对于指导资源和评估护理质量干预是重要的。目的是使用电子健康记录（EHR）数据（EHR）数据在非密集护理病房中开发和验证基于自动化的SEPSIS-3监测系统，并通过确定医院发作败血症和病房之间的变化来证明效用。方法采用从2012年7月至2013年7月至2013年12月间在学术中心入住的所有成年患者的群组中，使用EHR数据开发了基于规则的算法。审查了重症监护单位的时间。为了验证算法性能，根据SEPSIS-3临床标准（可疑感染与患有任何培养物，施用至少两剂抗微生物剂的涉嫌感染和至少两剂施用的患有培养物，施用任何培养物的感染感染和326名没有疑似感染的674人和326个没有疑似感染的326个没有疑似感染。并增加顺序器官失败评估（沙发）评分> 2分）以及医师医疗记录审查感染的可能性。结果总计82名653份住院入院。 Mepsis-3由医生审查确定的临床标准在1000张集中达到了343个。其中，313（91％）有可能，可能或明确的感染。基于该参考，算法达到灵敏度0.887（95％CI：0.799至0.964），特异性0.985（95％CI：0.978至0.991），阳性预测值0.881（95％CI：0.833至0.926）和负预测值0.986 （95％CI：0.973至0.996）。当考虑到总队列时考虑到具有和不具有可疑感染的人的抽样比例，算法确定了8599（10.4％）败血症发作。医院发作败血症的负担（>入院后48小时）和病房之间的医院内死亡率各不相同。结论使用EHR数据进行了全自动的SEPSIS-3监视算法，与非重症监护病房中的医生医疗记录综合进行了良好的良好监测算法，以及病房之间的医院发病败血症发病率的暴露变化。

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