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首页> 外文期刊>Pediatric critical care medicine: a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies >Performance of an Automated Screening Algorithm for Early Detection of Pediatric Severe Sepsis*
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Performance of an Automated Screening Algorithm for Early Detection of Pediatric Severe Sepsis*

机译：儿科严重脓毒症早期检测自动筛查算法的性能*

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摘要

Objectives: To create and evaluate a continuous automated alert system embedded in the electronic health record for the detection of severe sepsis among pediatric inpatient and emergency department patients. Design: Retrospective cohort study. The main outcome was the algorithm's appropriate detection of severe sepsis. Episodes of severe sepsis were identified by chart review of encounters with clinical interventions consistent with sepsis treatment, use of a diagnosis code for sepsis, or deaths. The algorithm was initially tested based upon criteria of the International Pediatric Sepsis Consensus Conference; we present iterative changes which were made to increase the positive predictive value and generate an improved algorithm for clinical use. Setting: A quaternary care, freestanding children's hospital with 404 inpatient beds, 70 ICU beds, and approximately 60,000 emergency department visits per year Patients: All patients less than 18 years presenting to the emergency department or admitted to an inpatient floor or ICU (excluding neonatal intensive care) between August 1, 2016, and December 28, 2016. Intervention: Creation of a pediatric sepsis screening algorithm. Measurements and Main Results: There were 288 (1.0%) episodes of severe sepsis among 29,010 encounters. The final version of the algorithm alerted in 9.0% (CI, 8.7-9.3%) of the encounters with sensitivity 72% (CI, 67-77%) for an episode of severe sepsis; specificity 91.8% (CI, 91.5-92.1%); positive predictive value 8.1% (CI, 7.0-9.2%); negative predictive value 99.7% (CI, 99.6-99.8%). Positive predictive value was highest in the ICUs (10.4%) and emergency department (9.6%). Conclusions: A continuous, automated electronic health record-based sepsis screening algorithm identified severe sepsis among children in the inpatient and emergency department settings and can be deployed to support early detection, although performance varied significantly by hospital location.
机译：目的：创建和评估嵌入在电子健康记录中的连续自动警报系统，用于检测儿科住院患者和急诊部患者的严重脓毒症。设计：回顾性队列研究。主要结果是算法适当检测严重败血症。通过对患有脓毒症治疗一致的临床干预措施的遇到遇到的遭遇，使用脓毒症或死亡的诊断码来确定严重脓毒症的剧集。算法最初根据国际儿科欲绝委员会协商一致意见的标准进行测试;我们提出了迭代变化，这是为了增加阳性预测值并产生改进的临床算法。环境：四季度护理，独立儿童医院，拥有404张住院患者，70张ICU床和每年患者约有60,000名急诊部门访问：所有患者均不到18岁，呈现给急诊部门或被录取到住院地板或ICU（不包括新生儿2016年8月1日至2016年12月28日之间的重症监护权。干预：创建儿科败血症筛查算法。测量和主要结果：29,010个遭遇中有288个（1.0％）的严重败血症发作。该算法的最终版本提醒9.0％（CI，8.7-9.3％）的遇到敏感性72％（CI，67-77％），用于严重败血症的一集;特异性91.8％（CI，91.5-92.1％）;阳性预测值8.1％（CI，7.0-9.2％）;负预测值99.7％（CI，99.6-99.8％）。 ICU（10.4％）和急诊部（9.6％）的阳性预测值最高。结论：连续，自动化的电子健康型脓毒症筛查算法鉴定了住院生和应急部门环境中儿童的严重脓毒症，并且可以部署以支持早期检测，但医院位置的性能显着多种多样。

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