ArticleThe Lancet regional health. Europe2021
A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children.
Article in The Lancet regional health. Europe, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Predicting sepsis treatment decisions in the paediatric emergency department using machine learning: the AiSEPTRON study.BMJ paediatrics open · 2025Observational
- Protocol for the diagnostic performance of C reactive protein, procalcitonin and interleukin-6 for serious bacterial infections among children ≤36 months old presenting with fever without source: a systematic review and meta-analysis.BMJ paediatrics open · 2024Article
- SBI is uncommon in the absence of paediatricians' gut feeling and abnormal respiratory pattern.Acta paediatrica (Oslo, Norway : 1992) · 2022Article
- Differentiating vaccine reactions from invasive bacterial infections in young infants presenting to the emergency department in the 4CMenB era: a retrospective observational comparison.BMJ paediatrics open · 2022Observational
- Febrile children with comorbidities at the emergency department - a multicentre observational study.European journal of pediatrics · 2022Observational
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Authors and funding
27 authors at 14 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProlonged 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.
methodsThe 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).
findingsOf 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.
interpretationPatient 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.
fundingEuropean Union, NIHR, NHS.
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