Rolnick Joshua, Downing N Lance, Shepard John, Chu Weihan, Tam Julia, Wessels Alexander, Li Ron, Dietrich Brian, Rudy Michael, Castaneda Leon, Shieh Lisa
Santa Clara Valley Medical Center.
Stanford Hospital & Clinics.
Appl Clin Inform. 2016 Jun 22;7(2):560-72. doi: 10.4338/ACI-2015-11-RA-0159. eCollection 2016.
Increasing use of EHRs has generated interest in the potential of computerized clinical decision support to improve treatment of sepsis. Electronic sepsis alerts have had mixed results due to poor test characteristics, the inability to detect sepsis in a timely fashion and the use of outside software limiting widespread adoption. We describe the development, evaluation and validation of an accurate and timely severe sepsis alert with the potential to impact sepsis management.
To develop, evaluate, and validate an accurate and timely severe sepsis alert embedded in a commercial EHR.
The sepsis alert was developed by identifying the most common severe sepsis criteria among a cohort of patients with ICD 9 codes indicating a diagnosis of sepsis. This alert requires criteria in three categories: indicators of a systemic inflammatory response, evidence of suspected infection from physician orders, and markers of organ dysfunction. Chart review was used to evaluate test performance and the ability to detect clinical time zero, the point in time when a patient develops severe sepsis.
Two physicians reviewed 100 positive cases and 75 negative cases. Based on this review, sensitivity was 74.5%, specificity was 86.0%, the positive predictive value was 50.3%, and the negative predictive value was 94.7%. The most common source of end-organ dysfunction was MAP less than 70 mm/Hg (59%). The alert was triggered at clinical time zero in 41% of cases and within three hours in 53.6% of cases. 96% of alerts triggered before a manual nurse screen.
We are the first to report the time between a sepsis alert and physician chart-review clinical time zero. Incorporating physician orders in the alert criteria improves specificity while maintaining sensitivity, which is important to reduce alert fatigue. By leveraging standard EHR functionality, this alert could be implemented by other healthcare systems.
电子健康记录(EHR)的使用日益增加,引发了人们对计算机化临床决策支持改善脓毒症治疗潜力的兴趣。由于测试特征不佳、无法及时检测脓毒症以及使用外部软件限制了广泛采用,电子脓毒症警报的结果参差不齐。我们描述了一种准确及时的严重脓毒症警报的开发、评估和验证,其有可能影响脓毒症管理。
开发、评估和验证嵌入商业EHR中的准确及时的严重脓毒症警报。
通过在一组诊断为脓毒症的国际疾病分类第九版(ICD 9)编码患者中确定最常见的严重脓毒症标准来开发脓毒症警报。此警报需要三类标准:全身炎症反应指标、医生医嘱中疑似感染的证据以及器官功能障碍标志物。通过病历审查来评估测试性能以及检测临床零时(患者发生严重脓毒症的时间点)的能力。
两名医生审查了100例阳性病例和75例阴性病例。基于此审查,敏感性为74.5%,特异性为86.0%,阳性预测值为50.3%,阴性预测值为94.7%。终末器官功能障碍最常见的来源是平均动脉压(MAP)低于70毫米汞柱(59%)。警报在41%的病例中于临床零时触发,在53.6%的病例中在三小时内触发。96%的警报在护士人工筛查之前触发。
我们首次报告了脓毒症警报与医生病历审查临床零时之间的时间间隔。在警报标准中纳入医生医嘱可提高特异性,同时保持敏感性,这对于减少警报疲劳很重要。通过利用标准的EHR功能,其他医疗系统可以实施此警报。