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The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia

BACKGROUND: Disposition decisions are critical to the functioning of Emergency Departments. The objectives of the present study were to derive and internally validate a prediction model for inpatient admission from the Emergency Department to assist with triage, patient flow and clinical decision ma...

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Autores principales: Dinh, Michael M., Russell, Saartje Berendsen, Bein, Kendall J., Rogers, Kris, Muscatello, David, Paoloni, Richard, Hayman, Jon, Chalkley, Dane R., Ivers, Rebecca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5135778/
https://www.ncbi.nlm.nih.gov/pubmed/27912757
http://dx.doi.org/10.1186/s12873-016-0111-4
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author Dinh, Michael M.
Russell, Saartje Berendsen
Bein, Kendall J.
Rogers, Kris
Muscatello, David
Paoloni, Richard
Hayman, Jon
Chalkley, Dane R.
Ivers, Rebecca
author_facet Dinh, Michael M.
Russell, Saartje Berendsen
Bein, Kendall J.
Rogers, Kris
Muscatello, David
Paoloni, Richard
Hayman, Jon
Chalkley, Dane R.
Ivers, Rebecca
author_sort Dinh, Michael M.
collection PubMed
description BACKGROUND: Disposition decisions are critical to the functioning of Emergency Departments. The objectives of the present study were to derive and internally validate a prediction model for inpatient admission from the Emergency Department to assist with triage, patient flow and clinical decision making. METHODS: This was a retrospective analysis of State-wide Emergency Department data in New South Wales, Australia. Adult patients (age ≥ 16 years) were included if they presented to a Level five or six (tertiary level) Emergency Department in New South Wales, Australia between 2013 and 2014. The outcome of interest was in-patient admission from the Emergency Department. This included all admissions to short stay and medical assessment units and being transferred out to another hospital. Analyses were performed using logistic regression. Discrimination was assessed using area under curve and derived risk scores were plotted to assess calibration. RESULTS: 1,721,294 presentations from twenty three Level five or six hospitals were analysed. Of these 49.38% were male and the mean (sd) age was 49.85 years (22.13). Level 6 hospitals accounted for 47.70% of cases and 40.74% of cases were classified as an in-patient admission based on their mode of separation. The final multivariable model including age, arrival by ambulance, triage category, previous admission and presenting problem had an AUC of 0.82 (95% CI 0.81, 0.82). CONCLUSION: By deriving and internally validating a risk score model to predict the need for in-patient admission based on basic demographic and triage characteristics, patient flow in ED, clinical decision making and overall quality of care may be improved. Further studies are now required to establish clinical effectiveness of this risk score model.
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spelling pubmed-51357782016-12-15 The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia Dinh, Michael M. Russell, Saartje Berendsen Bein, Kendall J. Rogers, Kris Muscatello, David Paoloni, Richard Hayman, Jon Chalkley, Dane R. Ivers, Rebecca BMC Emerg Med Research Article BACKGROUND: Disposition decisions are critical to the functioning of Emergency Departments. The objectives of the present study were to derive and internally validate a prediction model for inpatient admission from the Emergency Department to assist with triage, patient flow and clinical decision making. METHODS: This was a retrospective analysis of State-wide Emergency Department data in New South Wales, Australia. Adult patients (age ≥ 16 years) were included if they presented to a Level five or six (tertiary level) Emergency Department in New South Wales, Australia between 2013 and 2014. The outcome of interest was in-patient admission from the Emergency Department. This included all admissions to short stay and medical assessment units and being transferred out to another hospital. Analyses were performed using logistic regression. Discrimination was assessed using area under curve and derived risk scores were plotted to assess calibration. RESULTS: 1,721,294 presentations from twenty three Level five or six hospitals were analysed. Of these 49.38% were male and the mean (sd) age was 49.85 years (22.13). Level 6 hospitals accounted for 47.70% of cases and 40.74% of cases were classified as an in-patient admission based on their mode of separation. The final multivariable model including age, arrival by ambulance, triage category, previous admission and presenting problem had an AUC of 0.82 (95% CI 0.81, 0.82). CONCLUSION: By deriving and internally validating a risk score model to predict the need for in-patient admission based on basic demographic and triage characteristics, patient flow in ED, clinical decision making and overall quality of care may be improved. Further studies are now required to establish clinical effectiveness of this risk score model. BioMed Central 2016-12-03 /pmc/articles/PMC5135778/ /pubmed/27912757 http://dx.doi.org/10.1186/s12873-016-0111-4 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Dinh, Michael M.
Russell, Saartje Berendsen
Bein, Kendall J.
Rogers, Kris
Muscatello, David
Paoloni, Richard
Hayman, Jon
Chalkley, Dane R.
Ivers, Rebecca
The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia
title The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia
title_full The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia
title_fullStr The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia
title_full_unstemmed The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia
title_short The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia
title_sort sydney triage to admission risk tool (start) to predict emergency department disposition: a derivation and internal validation study using retrospective state-wide data from new south wales, australia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5135778/
https://www.ncbi.nlm.nih.gov/pubmed/27912757
http://dx.doi.org/10.1186/s12873-016-0111-4
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