A simple tool to predict admission at the time of triage

Abstract

AimTo create and validate a simple clinical score to estimate the probability of admission at the time of triage.MethodsThis was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests.Results215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p<0.0001).ConclusionsThis simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to ‘admission likely’, ‘admission unlikely’, ‘admission very unlikely’ etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments.

The presenting and prescribing patterns of migraine in an Australian emergency department: A descriptive exploratory study

“…[10] Arrival characteristics to the Emergency Department, such as time of arrival, mode of arrival, and triage category, may be useful prognostic factors for outcomes such as probability of admission, length of stay, or mortality. [11] There are few publications regarding ED arrival characteristics of migraine patients.…”

Design and evaluation of analytical tools for emergency department management based on machine learning techniques

“…Also, a system capable of accurately establishing the probabilities of inpatient admission (hospitalization) for every ED patient right after triage can help streamline operations and establish priorities for clinical personnel, bed managers and supporting personnel. Previous research had shown that clinical triage 1 personnel could not predict the need for inpatient admission with sufficient reliability [21][22][23], however, models with a manageable number of easily-obtainable variables and a simple procedure for calculating the probabilities of admission could be used to aid in this task [24][25][26]. In both cases (ED census forecasts and prediction of probabilities of inpatient admission from the ED) predictive models with varying degrees of sophistication can be developed.…”

“…Examples of these are the total number of pharmacy dispensations in primary care [24] for a given patient, or standardized chronic clinical conditions [25], which are not available in all EDs of specialized care hospitals in real time (or at all). Also, several models relied on variables which were highly specific to a country or a region [25,26], which made them impossible to apply in other regional settings.…”

“…Most of the previous models also exclude large groups of patients such as pediatric [26] or non-geriatric [24] patients. Our goal was to develop a predictive system for patients of all age groups and all Manchester…”

“…(iii) A large-sample prediction model for admission from the ED, reliant on variables available right at the end of the MTS triage process, was recently developed in the United Kingdom [26]. A 6-variable scoring method was derived from a logistic regression model.…”