RSVpredict: An Online Tool to Calculate the Likelihood of Respiratory Syncytial Virus Infection in Children Hospitalized With Acute Respiratory Disease. Issue 7 (July 2019)

• Record Type: Journal Article
• Authors: Manuel, Britta; Hackbusch, Matthes; Tabatabai, Julia; Hoos, Johannes; Peters, Rebecca; Valerie Schnee, Sarah; Marlene Ihling, Clara; Schnitzler, Paul; Pfeil, Johannes
• Is Part Of: Pediatric infectious disease journal. Volume 38:Issue 7(2019)
• Publication Date: 2019-07
• Subjects: respiratory syncytial virus -- RSV epidemiology -- RSV risk score -- prediction model -- acute respiratory tract infection -- respiratory tract infection -- RSVpredict; Communicable diseases in children -- Periodicals; Infection in children -- Periodicals; 618.929
• DOI: 10.1097/INF.0000000000002283
• Languages: English
• ISSNs: 0891-3668

Abstract

Abstract : Background: Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory tract infection in young children. Early detection of RSV infection can avoid unnecessary diagnostic and therapeutic intervention and is required to prevent the nosocomial spread of RSV infection in pediatric hospitals. We developed a web tool to calculate the probability of RSV infection in children hospitalized with acute respiratory tract infection (ARTI) ( RSVpredict ). Methods: During winter seasons 2014/2015 to 2017/2018, 1545 children hospitalized with clinical symptoms of ARTI at the University Hospital Heidelberg/Germany were prospectively included. Medical information was reported on a standardized data sheet, and nasopharyngeal swabs were obtained for multiplex real-time polymerase chain reaction analyses. We applied logistic regression to develop a prediction model and developed a web-based application to predict the individual probability of RSV infection. Results: Duration of clinical symptoms ≥2 days on admission, calendar month of admission, admission for lower respiratory tract infection, the presence of cough and rale and younger age were associated with RSV infection ( P < 0.05). Those data were included in the prediction model ( RSVpredict, https://web.imbi.uni-heidelberg.de/rsv/ ). RSVpredict is a web-based application to calculate the risk of RSV infection in children hospitalized with ARTI. The prediction model is based on easily accessible clinical symptoms and predicts the individual probability of RSV infection risk immediately. Conclusions: Pediatricians might use the RSVpredict to take informed decisions on further diagnostic and therapeutic intervention, including targeted RSV testing in children with relevant RSV infection risk. Abstract : Supplemental Digital Content is available in the text.