A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background
Prolonged Emergency Department (ED) stay causes crowding and negatively impacts quality of care. We developed and validated a prediction model for early identification of febrile children with a high risk of hospitalisation in order to improve ED flow.
Methods
The MOFICHE study prospectively collected data on febrile children (0–18 years) presenting to 12 European EDs. A prediction models was constructed using multivariable logistic regression and included patient characteristics available at triage. We determined the discriminative values of the model by calculating the area under the receiver operating curve (AUC).
Findings
Of 38,424 paediatric encounters, 9,735 children were admitted to the ward and 157 to the PICU. The prediction model, combining patient characteristics and NICE alarming, yielded an AUC of 0.84 (95%CI 0.83-0.84).
The model performed well for a rule-in threshold of 75% (specificity 99.0% (95%CI 98.9-99.1%, positive likelihood ratio 15.1 (95%CI 13.4-17.1), positive predictive value 0.84 (95%CI 0.82-0.86)) and a rule-out threshold of 7.5% (sensitivity 95.4% (95%CI 95.0-95.8), negative likelihood ratio 0.15 (95%CI 0.14-0.16), negative predictive value 0..95 (95%CI 0.95-9.96)). Validation in a separate dataset showed an excellent AUC of 0.91 (95%CI 0.90- 0.93). The model performed well for identifying children needing PICU admission (AUC 0.95, 95%CI 0.93-0.97). A digital calculator was developed to facilitate clinical use.
Interpretation
Patient characteristics and NICE alarming signs available at triage can be used to identify febrile children at high risk for hospitalisation and can be used to improve ED flow.
Funding
European Union, NIHR, NHS.
Prolonged Emergency Department (ED) stay causes crowding and negatively impacts quality of care. We developed and validated a prediction model for early identification of febrile children with a high risk of hospitalisation in order to improve ED flow.
Methods
The MOFICHE study prospectively collected data on febrile children (0–18 years) presenting to 12 European EDs. A prediction models was constructed using multivariable logistic regression and included patient characteristics available at triage. We determined the discriminative values of the model by calculating the area under the receiver operating curve (AUC).
Findings
Of 38,424 paediatric encounters, 9,735 children were admitted to the ward and 157 to the PICU. The prediction model, combining patient characteristics and NICE alarming, yielded an AUC of 0.84 (95%CI 0.83-0.84).
The model performed well for a rule-in threshold of 75% (specificity 99.0% (95%CI 98.9-99.1%, positive likelihood ratio 15.1 (95%CI 13.4-17.1), positive predictive value 0.84 (95%CI 0.82-0.86)) and a rule-out threshold of 7.5% (sensitivity 95.4% (95%CI 95.0-95.8), negative likelihood ratio 0.15 (95%CI 0.14-0.16), negative predictive value 0..95 (95%CI 0.95-9.96)). Validation in a separate dataset showed an excellent AUC of 0.91 (95%CI 0.90- 0.93). The model performed well for identifying children needing PICU admission (AUC 0.95, 95%CI 0.93-0.97). A digital calculator was developed to facilitate clinical use.
Interpretation
Patient characteristics and NICE alarming signs available at triage can be used to identify febrile children at high risk for hospitalisation and can be used to improve ED flow.
Funding
European Union, NIHR, NHS.
Date Issued
2021-09-01
Date Acceptance
2021-06-24
Citation
The Lancet Regional Health - Europe, 2021, 8
ISSN
2666-7762
Publisher
Elsevier
Journal / Book Title
The Lancet Regional Health - Europe
Volume
8
Copyright Statement
© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Sponsor
National Institute of Health and Medical Research
European Commission
Identifier
https://www.sciencedirect.com/science/article/pii/S2666776221001502
Grant Number
CL-2018-21-007
Horizon 2020
Subjects
Admission prediction
Crowding
Emgerency Department
Febrile children
PERFORM consortium: Personalised Risk assessment in febrile children to optimise Real-life Management across the European Union
Publication Status
Published
Article Number
ARTN 100173
Date Publish Online
2021-07-12