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Journal of General and Emergency Medicine

JGEM

Aabstract

Research Article

Prediction Models for Admission of Infants Under 6 Months of Age Presenting with Non-specific Complaints

Michael Zhang, Marie-Louise Ratican, Valerie Astle, Nicholas Marriage, Jonathan Ash, Haddijatou Hughes

Correspondence Address :

Michael Zhang
Emergency Physician, School of Medicine and Public Health
University of Newcastle
Australia
Email : michael.zhang@hnehealth.nsw.gov.au

Received on: February 11, 2017, Accepted on: March 23, 2017 , Published on: March 30, 2017

Citation: Michael Zhang, Marie-Louise Ratican, Valerie Astle, Nicholas Marriage, Jonathan Ash, Haddijatou Hughes (2017). Prediction Models for Admission Of Infants Under 6 Months Of Age Presenting with Non-specific Complaints.

Copyright: 2017 Michael Zhang, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • Abstract

Abstract
Objective 
When young infants are brought to ED for assessment with non-specific complaints, it may be difficult to distinguish those with mild illness from those with serious underlying infection requiring admission. We hypothesize that prediction models for admission can be derived from the features manifested by the infants presenting to the ED. 
Methods 
This retrospective observational study examined a primary outcome of admission rate in infants under 6 months presenting to a mixed ED in the 2013-14 period. Clinical data of eligible patients were extracted from electronic medical record to construct statistical models of risks for admission. 
Results
Around 30% of infants with non-specific complaints were admitted for further management in the hospital. Multivariate modelling based on clinical data created a Combined model (AUC 0.965) that explained the variation of admission of these infants better than the SPOC model (AUC 0.702), Clinical model (AUC 0.862) and Flow model (AUC 0.925) (p < 0.001).In the Combined model, the biggest predictors for admission were necessity for blood test (OR 20.52, 95% CI 11.32 - 32.17), arrival by ambulance (OR 7.99, 95% CI 3.23 - 19.75) and maximum body temperature (OR 1.82, 95% CI 1.31 - 2.53) of the presenting infants. In contrast, the infants waiting longer periods of time to be seen by ED doctors were more likely to be discharged. 
Conclusions
Prediction models with excellent accuracy can be built on the features of young infants presenting with non-specific complaints. They could be used to guide decision making on the management of this group of patients in the ED. 
Keywords: Prediction Models; Admission; Infants; Non-specific Complaints;

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