A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children.
Admission prediction
Crowding
Emgerency Department
Febrile children
Journal
The Lancet regional health. Europe
ISSN: 2666-7762
Titre abrégé: Lancet Reg Health Eur
Pays: England
ID NLM: 101777707
Informations de publication
Date de publication:
Sep 2021
Sep 2021
Historique:
entrez:
24
9
2021
pubmed:
25
9
2021
medline:
25
9
2021
Statut:
epublish
Résumé
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. 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). 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. 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. European Union, NIHR, NHS.
Sections du résumé
BACKGROUND
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
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
RESULTS
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
CONCLUSIONS
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
BACKGROUND
European Union, NIHR, NHS.
Identifiants
pubmed: 34557857
doi: 10.1016/j.lanepe.2021.100173
pii: S2666-7762(21)00150-2
pmc: PMC8454797
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100173Informations de copyright
© 2021 The Author(s).
Déclaration de conflit d'intérêts
DB, UB, EC, JD, ME, MF, NH, BK, FMT, HM, EL, ML, MP, IRC, FS, MT, CV, SY, DZ and WZ report grants from the European Union. Horizon 2020 research and innovation programme during the study conduct. FS reports a grant from the Slovenian Research Agency outside the submitted work. MP reports a grant from Pfizer and financial support from Pfizer and Sanofi outside the submitted work. MF reports a grant from CSL Behring outside the submitted work. RN reports a grant from the National Institute for Health Research during the study conduct. ME reports financial support from the National Institute for Health Research Biomedical Research Centre based at Newcastle Hospitals NHS Foundation Trust and Newcastle University ng the study conduct. MT is a member of the Advisory Board of MSD and Pfizer, a member of the National Committee on Immunization Practices and a member of the national Scientific Advisory Group for the management of the pandemic. All other authors declare no competing interests.
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