@article{3031311,
    title = "A NICE combination for predicting hospitalisation at the Emergency
Department: a European multicentre observational study of febrile
children",
    author = "Borensztajn, Dorine M. and Hagedoorn, Nienke N. and Carrol, Enitan D. and and von Both, Ulrich and Dewez, Juan Emmanuel and Emonts, Marieke and and van der Flier, Michiel and de Groot, Ronald and Herberg, Jethro and and Kohlmaier, Benno and Lim, Emma and Maconochie, Ian K. and and Martinon-Torres, Federico and Nieboer, Daan and Nijman, Ruud G. and and Oostenbrink, Rianne and Pokorn, Marko and Rivero Calle, Irene and Strle, and Franc and Tsolia, Maria and Vermont, Clementien L. and Yeung, Shunmay and and Zavadska, Dace and Zenz, Werner and Levin, Michael and Moll, and Henriette A. and PERFORM Consortium Personalised Ri",
    journal = "LANCET REGIONAL HEALTH-EUROPE",
    year = "2021",
    volume = "8",
    publisher = "Elsevier",
    doi = "10.1016/j.lanepe.2021.100173",
    keywords = "Emgerency Department; Febrile children; Crowding; Admission prediction",
    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 calculat-ing 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
likeli-hood 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 predic-tive 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. (c) 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/)"
}