Imperial College London
Dr Ian Maconochie

DrIanMaconochie

Faculty of Medicine, Department of Infectious Disease

Professor of Practice (Paediatric Emergency Medicine)
 
 
 
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Contact

 

+44 (0)20 3312 3729i.maconochie

 
 
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Location

 

Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nijman:2021:10.1371/journal.pone.0254366,
author = {Nijman, R and Borensztajn, D and Zachariasse, J and Hajema, C and Freitas, P and Greber-Platzer, S and Smit, F and Alves, C and van, der Lei J and Steyerberg, E and Maconochie, I and Moll, H},
doi = {10.1371/journal.pone.0254366},
journal = {PLoS One},
pages = {1--19},
title = {A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: a prospective multicentre observational study},
url = {http://dx.doi.org/10.1371/journal.pone.0254366},
volume = {16},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundTo develop a clinical prediction model to identify children at risk for revisits with serious illness to the emergency department.Methods and findingsA secondary analysis of a prospective multicentre observational study in five European EDs (the TRIAGE study), including consecutive children aged <16 years who were discharged following their initial ED visit (‘index’ visit), in 2012–2015. Standardised data on patient characteristics, Manchester Triage System urgency classification, vital signs, clinical interventions and procedures were collected. The outcome measure was serious illness defined as hospital admission or PICU admission or death in ED after an unplanned revisit within 7 days of the index visit. Prediction models were developed using multivariable logistic regression using characteristics of the index visit to predict the likelihood of a revisit with a serious illness. The clinical model included day and time of presentation, season, age, gender, presenting problem, triage urgency, and vital signs. An extended model added laboratory investigations, imaging, and intravenous medications. Cross validation between the five sites was performed, and discrimination and calibration were assessed using random effects models. A digital calculator was constructed for clinical implementation. 7,891 children out of 98,561 children had a revisit to the ED (8.0%), of whom 1,026 children (1.0%) returned to the ED with a serious illness. Rates of revisits with serious illness varied between the hospitals (range 0.7–2.2%). The clinical model had a summary Area under the operating curve (AUC) of 0.70 (95% CI 0.65–0.74) and summary calibration slope of 0.83 (95% CI 0.67–0.99). 4,433 children (5%) had a risk of > = 3%, which was useful for ruling in a revisit with serious illness, with positive likelihood ratio 4.41 (95% CI 3.87–5.01) and specificity 0.96 (95% CI 0.95–0.96). 37,546 (39%) had a risk <0.5%, whi
AU  - Nijman,R
AU  - Borensztajn,D
AU  - Zachariasse,J
AU  - Hajema,C
AU  - Freitas,P
AU  - Greber-Platzer,S
AU  - Smit,F
AU  - Alves,C
AU  - van,der Lei J
AU  - Steyerberg,E
AU  - Maconochie,I
AU  - Moll,H
DO  - 10.1371/journal.pone.0254366
EP  - 19
PY  - 2021///
SN  - 1932-6203
SP  - 1
TI  - A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: a prospective multicentre observational study
T2  - PLoS One
UR  - http://dx.doi.org/10.1371/journal.pone.0254366
UR  - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254366
UR  - http://hdl.handle.net/10044/1/90697
VL  - 16
ER  -
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