Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Brown, Samuel; Jones, Jason; Kuttler, Kathryn; Keddington, Roger; Allen, Todd; Haug, Peter
• Is Part Of: BMC emergency medicine. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 7
• Publication Date: 2016-12
• Subjects: Sepsis -- Automated detection -- Bayesian classifier; Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1186/s12873-016-0095-0
• Languages: English
• ISSNs: 1471-227X

Abstract

Abstract Background Sepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data. Methods We developed the model using retrospective data for 132, 748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93, 733 ED encounters from April 2009 through June 2010. Results The final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %. Conclusions We developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.