Development and Validation of an Automated Sepsis Risk Assessment System.
Aggressive resuscitation can decrease sepsis mortality, but its success depends on early detection of sepsis. The purpose of this study was to develop and verify an Automated Sepsis Risk Assessment System (Auto-SepRAS), which would automatically assess the sepsis risk of inpatients by applying data mining techniques to electronic health records (EHR) data and provide daily updates. The seven predictors included in the Auto-SepRAS after initial analysis were admission via the emergency department, which had the highest odds ratio; diastolic blood pressure; length of stay; respiratory rate; heart rate; and age. Auto-SepRAS classifies inpatients into three risk levels (high, moderate, and low) based on the predictive values from the sepsis risk-scoring algorithm. The sepsis risk for each patient is presented on the nursing screen of the EHR. The AutoSepRAS was implemented retrospectively in several stages using EHR data and its cut-off scores adjusted. Overall discrimination power was moderate (AUC>.80). The Auto-SepRAS should be verified or updated continuously or intermittently to maintain high predictive performance, but it does not require invasive tests or data input by nurses that would require additional time. Nurses are able to provide patients with nursing care appropriate to their risk levels by using the sepsis risk information provided by the Auto-SepRAS. In particular, with early detection of changes related to sepsis, nurses should be able to help in providing rapid initial resuscitation of high-risk patients. © 2016 Wiley Periodicals, Inc.
Clinical Nurse, Department of Intensive Care, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
Ji-Sun
JS
Back
Research Assistant, College of Nursing, The Catholic University of Korea, Seoul, Republic of Korea.
Yinji
Y
Jin
Research Assistant, College of Nursing, The Catholic University of Korea, Seoul, Republic of Korea.
Taixian
T
Jin
Professor, College of Nursing, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul, 137-701, Republic of Korea. leesunmi@catholic.ac.kr.
Sun-Mi
SM
Lee
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D057189:Checklist; D002648:Child; D002675:Child, Preschool; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D018805:Sepsis / Q000517:prevention & control*; D012720:Severity of Illness Index
computerized patient records; data mining; predictive validity; risk assessment; sepsis