View Full Chapter Figures Only Tables Only Videos Only Print Get Citation Citation AMA Citation Lin M, Ross C. Lin M, Ross C Lin, Matthew, and Cordelia Ross. "Clinical and biomarker-based diagnostic model identifies serious bacterial infections." 2 Minute Medicine, 7 July 2015. McGraw-Hill, New York, NY, 2015. AccessMedicine. http://accessmedicine.mhmedical.com/updatesContent.aspx?gbosid=373544§ionid=165238658 MLA Citation Lin M, Ross C. Lin M, Ross C Lin, Matthew, and Cordelia Ross.. "Clinical and biomarker-based diagnostic model identifies serious bacterial infections." 2 Minute Medicine New York, NY: McGraw-Hill, 2015, http://accessmedicine.mhmedical.com/updatesContent.aspx?gbosid=373544§ionid=165238658. Download citation file: RIS (Zotero) EndNote BibTex Medlars ProCite RefWorks Reference Manager Mendeley © Copyright Top Return Clip Autosuggest Results Clinical and biomarker-based diagnostic model identifies serious bacterial infections by Matthew Lin, Cordelia Ross, MD, MS +Originally published by 2 Minute Medicine® (view original article). Reused on AccessMedicine with permission. +1. Researchers derived a multivariable model, which included clinical and biomarker variables, found to be internally validated in terms of its ability to discriminate between pneumonia vs. no serious bacterial infection (SBI), and SBI vs. no SBI. +2. A diagnostic model for identifying SBIs, previous described by Nijman et al in 2011, was externally validated and was found to have increased accuracy when biomarkers such as procalcitonin and resistin were added to the model. +Evidence Rating: 1 (Excellent) Study Rundown: + +For pediatric patients, acute febrile illness is a leading reason for emergency department visits. While the majority of these presentations are due to benign, self-limiting illnesses, a small percentage of these patients will have a SBI. At present, there is no universal model available for diagnosing SBIs. In this prospective, diagnostic accuracy study, researchers aimed to derived a multivariable model that incorporated clinical and biomarker variables and to assess its accuracy in diagnosing SBIs in febrile children <16 years of age. Additionally, using their own data set, researchers externally validated a model previously published by Nijman et al in 2011, that included predefined variables of age, duration of fever, tachycardia, temperature, tachypnea, ill-appearance, retractions, prolonged capillary refill, oxygenation saturation <94%, and C-reaction protein (CRP). The researchers’ newly derived diagnostic model performed well on internal validation in discriminating between pneumonia vs. no SBI and SBI vs. no SBI. Nijman et al’s model was externally validated with this patient population, and the discriminatory ability increased for pneumonia vs. no SBI and SBI vs. no SBI when the model was expanded to include procalcitonin and resistin. This study suggests that risk prediction models that incorporate clinical and biochemical markers may have utility in identifying SBIs, and thus in improving decision-making in the management of febrile children in the ER. +Click to read the study, published today in Pediatrics +Relevant Reading: Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: a diagnostic study In-Depth [cross-sectional study]: + +This single-center prospective diagnostic accuracy study identified 1872 eligible subjects, of whom 1101 were recruited for analysis between November 2010 and April 2012. The study included children <16 years of age with fever (>38C) or history of fever who required blood tests as part of clinical management. Children with primary immunodeficiency were excluded. Diagnosis of SBI was made independently by both a research fellow and infectious disease consultant, based on composite reference standards that incorporated clinical, microbiological and radiologic data. Of 1101 subjects, 264 (24%) had SBIs. Researchers evaluated their own model using concordance statistics (c statistic), a number equal to the area under the receiver operator curve. Their derived model discriminated well on internal validation (c statistic 0.84; 95% CI = 0.78-0.90 for pneumonia; and 0.77, 95% CI = 0.71-0.83 for other SBIs). The Nijman model showed good discrimination between SBI and no SBI (c statistic=0.76), but stronger discrimination between pneumonia and no SBI (c statistic=0.85). When the model was expanded to include procalcitonin and resistin, discriminatory strength increased for distinguishing pneumonia vs. no SBI (c statistic=0.90) and SBI vs. no SBI (c statistic=0.84). Of the 1101 subjects, approximately 80% were admitted to the hospital and received antibiotics, with 60% not actually having SBIs. +©2017 2 Minute Medicine, Inc. All rights reserved. No works may be reproduced without expressed written consent from 2 Minute Medicine, Inc. Inquire about licensing here. No article should be construed as medical advice and is not intended as such by the authors or by 2 Minute Medicine, Inc.