Diagnosing serious infections in acutely ill children in ambulatory care (ERNIE 2 study protocol, part A): Diagnostic accuracy of a clinical decision tree and added value of a point-of-care C-reactive protein test and oxygen saturation
Verbakel JY., Lemiengre MB., De Burghgraeve T., De Sutter A., Bullens DMA., Aertgeerts B., Buntinx F., Aertgeerts B., Bullens D., Buntinx F., De Baets F., Decaestecker K., De Schynkel K., Lemiengre M., Logghe K., Leus J., Pattyn L., Raes M., Van den Berghe L., Van Geet C., Verbakel J.
Â© 2014 Verbakel et al. Background: Acute illness is the most common presentation of children to ambulatory care. In contrast, seriousinfections are rare and often present at an early stage. To avoid complications or death, early recognition andadequate referral are essential. In a recent large study children were included prospectively to construct asymptom-based decision tree with a sensi tivity and negative predictive value of nearly 100%. To reduce the numberof false positives, point-of-care tests might be useful, providing an immediate result at bedside. The most probablecandidate is C-reactive protein, as well as a pulse oximetry.Methods: This is a diagnostic accuracy study of signs, symptoms and point-of-care tests for serious infections.Acutely ill children presenting to a family physician or paediatrician will be included consecutively in Flanders,Belgium. Children testing positive on the decision tree will get a point-of-care C-reactive protein test. Children testingnegative will randomly either receive a point-of-care C-reactive protein test or usual care. The outcome of interest ishospital admission more than 24 hours with a serious infection within 10 days. Aiming to include over 6500 children,we will report the diagnostic accuracy of the decision tree (+/- the point-of-care C-reactive protein test or pulseoximetry) in sensitivity, specificity, positive and negative likelihood ratios, and positive and negative predictive values.New diagnostic algorithms will be constructed through classification and regression tree and multiple logisticregression analysis.Discussion: We aim to improve detection of serious infections, and present a practical tool for diagnostic triage ofacutely ill children in primary care. We also aim to reduce the number of investigations and admissions in childrenwith non-serious infections.Trial Registration: ClinicalTrials.gov Identifier: NCT02024282.