BMJ Open (Dec 2020)

Predicting severe pneumonia in the emergency department: a global study of the Pediatric Emergency Research Networks (PERN)—study protocol

  • Franz E Babl,
  • Stuart R Dalziel,
  • Simon Craig,
  • Santiago Mintegi,
  • Mark Neuman,
  • Amy C Plint,
  • Meredith L Borland,
  • Amit Kochar,
  • Naveen Poonai,
  • Fabio Midulla,
  • Shefali Jani,
  • Mihai Gafencu,
  • Shane George,
  • Arjun Rao,
  • Nathan Kuppermann,
  • Shu-Ling Chong,
  • Bruce Wright,
  • Annick Galetto-Lacour,
  • Andrea K Morrison,
  • Michelle Eckerle,
  • Jennifer Tucker,
  • James Chamberlain,
  • Nicholas Watkins,
  • Mark I Neuman,
  • Susanne Greber-Platzer,
  • Todd Adam Florin,
  • Daniel Joseph Tancredi,
  • Lilliam Ambroggio,
  • Fahd A Ahmad,
  • Andrea Álvarez-Álvarez,
  • Alberto Arrighini,
  • Usha Avva,
  • Elena Aquino Olivia,
  • Uchechi Azubuine,
  • Luisa Baron Gonzalez de Suso,
  • Kelly R Bergmann,
  • Stuart A Bradin,
  • Kristen Breslin,
  • Rosa María Calderón Checa,
  • Maria Natali Campo Fernández,
  • Carmen Campos-Calleja,
  • Kerry Caperell,
  • Pradip P Chaudhari,
  • Jonathan Cherry,
  • Wee-Jhong Chua,
  • Ida Concha Murray,
  • Thosar Deepali,
  • Pinky-Rose Espina,
  • Susan Fairbrother,
  • Alexandria Farish,
  • Daniel M Fein,
  • Ramón Fernández Álvarez,
  • Todd A Florin,
  • Stephen Freedman,
  • Karen Forward,
  • Jara Gaitero Tristán,
  • Iker Gangoiti,
  • Michael A Gardiner,
  • Virginia Gómez-Barrena,
  • Tamara Hirsch Birn,
  • Adam Isacoff,
  • April J Kam,
  • Nirupama Kannikeswaran,
  • Maria Y Kwok,
  • Maren M Lunoe,
  • Ryan McKee,
  • Son H McLaren,
  • Lianne McLean,
  • Garth D Meckler,
  • Erin Mills,
  • Diana Aniela Moldovan,
  • Andrea Mora-Capín,
  • Viera Morales,
  • Claudia R Morris,
  • Nidhya Navanandan,
  • Rebecca Oglesby,
  • Ioannis Orfanos,
  • Sonia Viviana Pavlicich,
  • Astrid Pezoa Fuenzalida,
  • Mercè Puigdomènech Fosch,
  • Miguel Angelats Carlos Romero,
  • Vikram Sabhaney,
  • Cyril Sahyoun,
  • Frederic Samson,
  • Nipam P Shah,
  • Pilar Storch-de-Gracia Calvo,
  • Tristan Turner,
  • Muhammad Waseem,
  • Joseph Zorc

DOI
https://doi.org/10.1136/bmjopen-2020-041093
Journal volume & issue
Vol. 10, no. 12

Abstract

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Introduction Pneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.Methods and analysis This study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7–14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.Ethics and dissemination This study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children’s Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation.