Age in addition to RETTS triage priority substantially improves 3-day mortality prediction in emergency department patients: a multi-center cohort study.
Age factors
Emergency Medical Service
Emergency medicine
Hospital
Mortality
Observational study
Predictive value of tests
Primary complaint
RETTS
Risk factors
Triage
Journal
Scandinavian journal of trauma, resuscitation and emergency medicine
ISSN: 1757-7241
Titre abrégé: Scand J Trauma Resusc Emerg Med
Pays: England
ID NLM: 101477511
Informations de publication
Date de publication:
18 Oct 2023
18 Oct 2023
Historique:
received:
18
02
2023
accepted:
25
09
2023
medline:
23
10
2023
pubmed:
19
10
2023
entrez:
18
10
2023
Statut:
epublish
Résumé
Previous studies have shown varying results on the validity of the rapid emergency triage and treatment system (RETTS), but have concluded that patient age is not adequately considered as a risk factor for short term mortality. Little is known about the RETTS system's performance between different chief complaints and on short term mortality. We therefore aimed to evaluate how well a model including both RETTS triage priority and patient age (TP and age model) predicts 3-day mortality compared to a univariate RETTS triage priority model (TP model). Secondarily, we aimed to evaluate the TP model compared to a univariate age model (age model) and whether these three models' predictive performance regarding 3-day mortality varies between patients with different chief complaints in an unsorted emergency department patient population. This study was a prospective historic observational cohort study, using logistic regression on a cohort of patients seeking emergency department care in Stockholm during 2012-2016. Patient visits were stratified into the 10 chief complaint categories (CCC) with the highest number of deceased patients within 3 days of arrival, and to "other chief complaints". Patients with priority 1 were excluded. The studied cohort contained 1,690,981 visits by 788,046 different individuals. The TP and age model predicted 3-day mortality significantly and substantially better than both univariate models in the total population and in each studied CCC. The age model predicted 3-day mortality significantly and substantially better than the TP model in the total population and for all but three CCCs and was not inferior in any CCC. There were substantial differences between the studied CCCs in the predictive ability of each of the three models. Adding patient age to the RETTS triage priority system significantly and substantially improves 3-day mortality prediction compared to RETTS priority alone. Age alone is a non-inferior predictor of 3-day mortality compared to RETTS priority. The impact on 3-day mortality prediction of adding patient age to RETTS priority varies between CCCs but is substantial for all CCCs and for the total population. Including age as a variable in future revisions of RETTS could substantially improve patient safety.
Sections du résumé
BACKGROUND
BACKGROUND
Previous studies have shown varying results on the validity of the rapid emergency triage and treatment system (RETTS), but have concluded that patient age is not adequately considered as a risk factor for short term mortality. Little is known about the RETTS system's performance between different chief complaints and on short term mortality. We therefore aimed to evaluate how well a model including both RETTS triage priority and patient age (TP and age model) predicts 3-day mortality compared to a univariate RETTS triage priority model (TP model). Secondarily, we aimed to evaluate the TP model compared to a univariate age model (age model) and whether these three models' predictive performance regarding 3-day mortality varies between patients with different chief complaints in an unsorted emergency department patient population.
METHODS
METHODS
This study was a prospective historic observational cohort study, using logistic regression on a cohort of patients seeking emergency department care in Stockholm during 2012-2016. Patient visits were stratified into the 10 chief complaint categories (CCC) with the highest number of deceased patients within 3 days of arrival, and to "other chief complaints". Patients with priority 1 were excluded.
RESULTS
RESULTS
The studied cohort contained 1,690,981 visits by 788,046 different individuals. The TP and age model predicted 3-day mortality significantly and substantially better than both univariate models in the total population and in each studied CCC. The age model predicted 3-day mortality significantly and substantially better than the TP model in the total population and for all but three CCCs and was not inferior in any CCC. There were substantial differences between the studied CCCs in the predictive ability of each of the three models.
CONCLUSIONS
CONCLUSIONS
Adding patient age to the RETTS triage priority system significantly and substantially improves 3-day mortality prediction compared to RETTS priority alone. Age alone is a non-inferior predictor of 3-day mortality compared to RETTS priority. The impact on 3-day mortality prediction of adding patient age to RETTS priority varies between CCCs but is substantial for all CCCs and for the total population. Including age as a variable in future revisions of RETTS could substantially improve patient safety.
Identifiants
pubmed: 37853463
doi: 10.1186/s13049-023-01123-8
pii: 10.1186/s13049-023-01123-8
pmc: PMC10585720
doi:
Types de publication
Observational Study
Multicenter Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
55Informations de copyright
© 2023. Norwegian Air Ambulance Foundation.
Références
Scand J Trauma Resusc Emerg Med. 2011 Jun 30;19:42
pubmed: 21718476
Ann Emerg Med. 2009 May;53(5):605-11
pubmed: 19027193
Scand J Trauma Resusc Emerg Med. 2020 Aug 17;28(1):81
pubmed: 32807224
Scand J Trauma Resusc Emerg Med. 2021 Jul 3;29(1):89
pubmed: 34217351
CJEM. 2017 Jul;19(S2):S18-S27
pubmed: 28756800
Am J Emerg Med. 2021 Aug;46:508-514
pubmed: 33191046
Medicine (Baltimore). 2017 Nov;96(44):e8457
pubmed: 29095294
Acad Emerg Med. 1995 Nov;2(11):990-5
pubmed: 8536127
Ann Emerg Med. 2007 Mar;49(3):275-81
pubmed: 17141139
Acad Emerg Med. 2011 Dec;18(12):1358-70
pubmed: 22168200
PLoS One. 2018 Aug 30;13(8):e0203316
pubmed: 30161242
Ann Emerg Med. 2012 Sep;60(3):317-25.e3
pubmed: 22401951
J Am Coll Emerg Physicians Open. 2020 Sep 12;1(6):1312-1319
pubmed: 33392538
Scand J Trauma Resusc Emerg Med. 2016 Mar 03;24:21
pubmed: 26940235
PLoS One. 2020 Feb 20;15(2):e0229210
pubmed: 32078640
Biometrics. 1988 Sep;44(3):837-45
pubmed: 3203132
Clin Epidemiol. 2021 Jul 19;13:533-554
pubmed: 34321928
BMC Geriatr. 2019 May 23;19(1):139
pubmed: 31122186
Health Informatics J. 2020 Mar;26(1):34-44
pubmed: 30488755
Scand J Trauma Resusc Emerg Med. 2011 Nov 03;19:68
pubmed: 22050641
Scand J Trauma Resusc Emerg Med. 2022 Apr 15;30(1):27
pubmed: 35428351
BMC Bioinformatics. 2011 Mar 17;12:77
pubmed: 21414208
Eur J Epidemiol. 2017 Sep;32(9):765-773
pubmed: 28983736
PLoS One. 2021 Mar 10;16(3):e0247881
pubmed: 33690653
Ann Emerg Med. 2019 Jul;74(1):140-152
pubmed: 30470513