• Emily L Aaronson, Yuchiao Chang, and Pierre Borczuk.
• Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114. Electronic address: eaaronson@partners.org.
• Am J Emerg Med. 2016 Aug 1; 34 (8): 1354-8.

ObjectivePatients with abdominal diagnoses constitute 5% to 10% of all emergency department (ED) presentations. The goal of this study is to identify which of these patients will have a nonconcerning diagnosis based on demographic, physical examination, and basic laboratory testing.MethodsConsecutive patients from July 2013 to March 2014 discharged with a gastrointestinal (GI) diagnosis who presented to an urban, university-affiliated ED were identified. The cohort was split into a derivation set and a validation set. Using univariate and multivariable logistic regression analysis, a risk score was created based on the deviation data and then tested on the validation data.ResultsThere were 8852 patients with a GI diagnosis during the study period. A total of 7747 (87.5%) of them had a nonconcerning diagnosis. The logistic regression model identified 13 variables that predict a concerning GI diagnosis and created a scoring system ranging from 0 to 20. The area under the receiver operating characteristic was 0.81. When dichotomized at greater than or equal to 7 vs less than 7, the risk score has a sensitivity of 91% (95% confidence interval [CI], 88-94), specificity of 46% (95% CI, 44-48), positive predictive value of 17% (95% CI, 15-19) and negative predictive value of 98% (95% CI, 97-99).ConclusionOne can determine with a high degree of certainty, based only on an initial evaluation and screening laboratory work (excluding radiology) whether a patient who presents with a GI-related complaint has a nonconcerning diagnosis. This model could be used as a tool to aid in quality assurance when reviewing patients discharged with GI complaints and with future study, as a secondary triage instrument in a crowded ED environment, and aid in resource allocation.Copyright © 2016 Elsevier Inc. All rights reserved.

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