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. 2021 Jul 12:8:100173.
doi: 10.1016/j.lanepe.2021.100173. eCollection 2021 Sep.

A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children

Dorine M Borensztajn  1 , Nienke N Hagedoorn  1 , Enitan D Carrol  2   3   4 , Ulrich von Both  5 , Juan Emmanuel Dewez  6 , Marieke Emonts  7   8   9   10 , Michiel van der Flier  11   12   13 , Ronald de Groot  14 , Jethro Herberg  15   16 , Benno Kohlmaier  17 , Emma Lim  7   8 , Ian K Maconochie  15   16 , Federico Martinon-Torres  18 , Daan Nieboer  19 , Ruud G Nijman  15   16 , Rianne Oostenbrink  1 , Marko Pokorn  20 , Irene Rivero Calle  18 , Franc Strle  20 , Maria Tsolia  21 , Clementien L Vermont  22 , Shunmay Yeung  6 , Dace Zavadska  23 , Werner Zenz  17 , Michael Levin  15 , Henriette A Moll  1 ; PERFORM consortium: Personalised Risk assessment in febrile children to optimise Real-life Management across the European Union
Affiliations

A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children

Dorine M Borensztajn et al. Lancet Reg Health Eur. .

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.

Keywords: Admission prediction; Crowding; Emgerency Department; Febrile children.

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Conflict of interest statement

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.

Figures

Fig 1:
Fig. 1
ROC curves of the separate risk factors and combined model with patient characteristics, vital signs and NICE alarming signs Ia. Any admission 1b. Admission>24 hours 1c. PICU admission
Fig 2:
Fig. 2
Screenshot of digital admission risk calculator.
See this image and copyright information in PMC

References

    1. Leigh S, Robinson J, Yeung S, Coenen F, Carrol ED, Niessen LW. What matters when managing childhood fever in the emergency department? A discrete-choice experiment comparing the preferences of parents and healthcare professionals in the UK. Arch Dis Child. 2020 Feb 27. - PMC - PubMed
    1. Borensztajn D, Yeung S, Hagedoorn NN. Diversity in the emergency care for febrile children in Europe: a questionnaire study. BMJ Paediatr Open. 2019;3(1) - PMC - PubMed
    1. Foley M, Kifaieh N, Mallon WK. Financial impact of emergency department crowding. West J Emerg Med. 2011;12(2):192–197. May. - PMC - PubMed
    1. Pines JM, Batt RJ, Hilton JA, Terwiesch C. The financial consequences of lost demand and reducing boarding in hospital emergency departments. Ann Emerg Med. 2011;58(4):331–340. Oct. - PubMed
    1. Nilan LM, Patel L, Moffatt ME, Linebarger JS, Sherman AK, Randell KA. Compassion fatigue in pediatric emergency department staff. Pediatr Emerg Care. 2019;35(11):777–781. Nov. - PubMed

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