The Utility of Inflammatory and Endothelial Markers to Identify Infection in Emergency Department Patients. Issue 3 (September 2015)

• Record Type: Journal Article
• Authors: Day, Danielle E.; Oedorf, Kimie; Kogan, Slava; Novack, Victor; Sanchez, Leon D.; Wolfe, Richard E.; Shapiro, Nathan I.; Henning, Daniel J.
• Is Part Of: Shock. Volume 44:Issue 3(2015:Sep.)
• Publication Date: 2015-09
• Subjects: Shock -- Periodicals; Shock -- Periodicals; Choc (Pathologie) -- Périodiques; Shock; Periodicals; 616.0475
• DOI: 10.1097/SHK.0000000000000411
• Languages: English
• ISSNs: 1073-2322

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <sec> <title>ABSTRACT</title> <p> <bold>Background:</bold> Identifying infection in emergency department (ED) patients can be challenging. This study assesses the value that inflammatory and endothelial biomarkers add to clinical data when predicting infectious etiologies of abnormal vital signs (AVSs) in ED patients. <bold>Methods:</bold> This study was a prospective, observational cohort study of ED patients with AVSs at an urban, academic tertiary-care hospital, identified from March 1, 2013, to April 15, 2013. Collected blood samples were assayed for soluble E-selectin (sE-selectin), soluble intercellular adhesion molecule 1, vascular cell adhesion molecule 1, plasminogen activator inhibitor 1, interleukin 6, sFlt-1, and procalcitonin. History and physical examination were abstracted from the ED documentation. The primary outcome, infectious etiology, was adjudicated by review of the hospital documentation. Three multivariate logistic regression models predicting infection were created using clinical data, biomarkers, and combined clinical data and biomarker assessments. Integrated discrimination improvement tested the discriminate value of the biomarker and combined models compared with the clinical data model. <bold>Results:</bold> We enrolled 115 patients: 49 determined to have an infection (43%) and 66 without (57%). All biomarkers were significantly associated with infection in univariate analysis. The best clinical model (area under the curve [AUC] = 0.76) included initial temperature (odds ratio [OR], 1.6; confidence interval [CI], 1.1–2.2) and history of fever (OR, 5.0; CI, 1.4–14). The best biomarker model (AUC, 0.82) predicting infection included sE-selectin (OR, 11.0; 95% CI, 1.6–74) and interleukin 6 (OR, 5.1; CI, 2.3–11.6). The combined clinical and biomarker model had an AUC of 0.88, with integrated discrimination improvement = 0.21, compared with the clinical model alone. <bold>Conclusion:</bold> Inflammatory and endothelial markers can improve the clinical identification of infection in ED patients with AVSs.</p> </sec> </abstract>