The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study

The association between emergency department (ED) length of stay (EDLOS) with in-hospital mortality (IHM) in older patients remains unclear. This retrospective study aims to delineate the relationship between EDLOS and IHM in elderly patients. From the ED patients (n = 383,586) who visited an urban...

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Autores: Wu, Lijuan, Chen, Xuanhui, Khalemsky, Anna, Li, Deyang, Zoubeidi, Taoufik, Lauque, Dominique, Alsabri, Mohammed, Boudi, Zoubir, Kumar, Vijaya Arun, Paxton, James H., Tsilimingras, Dionyssios, Kurland, Lisa, Schwartz, David G., Hachimi-Idrissi, Said, Camargo, Carlos A., Liu, Shan W., Savioli, Gabriele, Intas, Geroge, Soni, Kapil Dev, Junhasavasdikul, Detajin, Trujillano Cabello, Javier, Rathlev, Niels K., Tazarourte, Karim, Slagman, Anna, Christ, Michael, Singer, Adam J., Lang, Eddy, Ricevuti, Giovanni, Li, Xin, Liang, Huiying, Grossman, Shamai A., Bellou, Abdelouahab
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/464303
Acceso en línea:https://doi.org/10.3390/jcm12144750
https://hdl.handle.net/10459.1/464303
Access Level:acceso abierto
Palabra clave:Emergency department
In-hospital mortality
Length of stay
Boarding time
Machine learning
Older adults
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spelling The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort StudyWu, LijuanChen, XuanhuiKhalemsky, AnnaLi, DeyangZoubeidi, TaoufikLauque, DominiqueAlsabri, MohammedBoudi, ZoubirKumar, Vijaya ArunPaxton, James H.Tsilimingras, DionyssiosKurland, LisaSchwartz, David G.Hachimi-Idrissi, SaidCamargo, Carlos A.Liu, Shan W.Savioli, GabrieleIntas, GerogeSoni, Kapil DevJunhasavasdikul, DetajinTrujillano Cabello, JavierRathlev, Niels K.Tazarourte, KarimSlagman, AnnaChrist, MichaelSinger, Adam J.Lang, EddyRicevuti, GiovanniLi, XinLiang, HuiyingGrossman, Shamai A.Bellou, AbdelouahabEmergency departmentIn-hospital mortalityLength of stayBoarding timeMachine learningOlder adultsThe association between emergency department (ED) length of stay (EDLOS) with in-hospital mortality (IHM) in older patients remains unclear. This retrospective study aims to delineate the relationship between EDLOS and IHM in elderly patients. From the ED patients (n = 383,586) who visited an urban academic tertiary care medical center from January 2010 to December 2016, 78,478 older patients (age ≥60 years) were identified and stratified into three age subgroups: 60-74 (early elderly), 75-89 (late elderly), and ≥90 years (longevous elderly). We applied multiple machine learning approaches to identify the risk correlation trends between EDLOS and IHM, as well as boarding time (BT) and IHM. The incidence of IHM increased with age: 60-74 (2.7%), 75-89 (4.5%), and ≥90 years (6.3%). The best area under the receiver operating characteristic curve was obtained by Light Gradient Boosting Machine model for age groups 60-74, 75-89, and ≥90 years, which were 0.892 (95% CI, 0.870-0.916), 0.886 (95% CI, 0.861-0.911), and 0.838 (95% CI, 0.782-0.887), respectively. Our study showed that EDLOS and BT were statistically correlated with IHM (p < 0.001), and a significantly higher risk of IHM was found in low EDLOS and high BT. The flagged rate of quality assurance issues was higher in lower EDLOS ≤1 h (9.96%) vs. higher EDLOS 7 h <t≤ 8 h (1.84%). Special attention should be given to patients admitted after a short stay in the ED and a long BT, and new concepts of ED care processes including specific areas and teams dedicated to older patients care could be proposed to policymakers.MDPI2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/jcm12144750https://hdl.handle.net/10459.1/464303reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a: https://doi.org/10.3390/jcm12144750Journal of Clinical Medicine, 2023, vol. 12, núm. 14cc-by (c)Authors, 2023Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/4643032026-06-24T12:42:17Z
dc.title.none.fl_str_mv The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
title The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
spellingShingle The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
Wu, Lijuan
Emergency department
In-hospital mortality
Length of stay
Boarding time
Machine learning
Older adults
title_short The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
title_full The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
title_fullStr The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
title_full_unstemmed The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
title_sort The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study
dc.creator.none.fl_str_mv Wu, Lijuan
Chen, Xuanhui
Khalemsky, Anna
Li, Deyang
Zoubeidi, Taoufik
Lauque, Dominique
Alsabri, Mohammed
Boudi, Zoubir
Kumar, Vijaya Arun
Paxton, James H.
Tsilimingras, Dionyssios
Kurland, Lisa
Schwartz, David G.
Hachimi-Idrissi, Said
Camargo, Carlos A.
Liu, Shan W.
Savioli, Gabriele
Intas, Geroge
Soni, Kapil Dev
Junhasavasdikul, Detajin
Trujillano Cabello, Javier
Rathlev, Niels K.
Tazarourte, Karim
Slagman, Anna
Christ, Michael
Singer, Adam J.
Lang, Eddy
Ricevuti, Giovanni
Li, Xin
Liang, Huiying
Grossman, Shamai A.
Bellou, Abdelouahab
author Wu, Lijuan
author_facet Wu, Lijuan
Chen, Xuanhui
Khalemsky, Anna
Li, Deyang
Zoubeidi, Taoufik
Lauque, Dominique
Alsabri, Mohammed
Boudi, Zoubir
Kumar, Vijaya Arun
Paxton, James H.
Tsilimingras, Dionyssios
Kurland, Lisa
Schwartz, David G.
Hachimi-Idrissi, Said
Camargo, Carlos A.
Liu, Shan W.
Savioli, Gabriele
Intas, Geroge
Soni, Kapil Dev
Junhasavasdikul, Detajin
Trujillano Cabello, Javier
Rathlev, Niels K.
Tazarourte, Karim
Slagman, Anna
Christ, Michael
Singer, Adam J.
Lang, Eddy
Ricevuti, Giovanni
Li, Xin
Liang, Huiying
Grossman, Shamai A.
Bellou, Abdelouahab
author_role author
author2 Chen, Xuanhui
Khalemsky, Anna
Li, Deyang
Zoubeidi, Taoufik
Lauque, Dominique
Alsabri, Mohammed
Boudi, Zoubir
Kumar, Vijaya Arun
Paxton, James H.
Tsilimingras, Dionyssios
Kurland, Lisa
Schwartz, David G.
Hachimi-Idrissi, Said
Camargo, Carlos A.
Liu, Shan W.
Savioli, Gabriele
Intas, Geroge
Soni, Kapil Dev
Junhasavasdikul, Detajin
Trujillano Cabello, Javier
Rathlev, Niels K.
Tazarourte, Karim
Slagman, Anna
Christ, Michael
Singer, Adam J.
Lang, Eddy
Ricevuti, Giovanni
Li, Xin
Liang, Huiying
Grossman, Shamai A.
Bellou, Abdelouahab
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Emergency department
In-hospital mortality
Length of stay
Boarding time
Machine learning
Older adults
topic Emergency department
In-hospital mortality
Length of stay
Boarding time
Machine learning
Older adults
description The association between emergency department (ED) length of stay (EDLOS) with in-hospital mortality (IHM) in older patients remains unclear. This retrospective study aims to delineate the relationship between EDLOS and IHM in elderly patients. From the ED patients (n = 383,586) who visited an urban academic tertiary care medical center from January 2010 to December 2016, 78,478 older patients (age ≥60 years) were identified and stratified into three age subgroups: 60-74 (early elderly), 75-89 (late elderly), and ≥90 years (longevous elderly). We applied multiple machine learning approaches to identify the risk correlation trends between EDLOS and IHM, as well as boarding time (BT) and IHM. The incidence of IHM increased with age: 60-74 (2.7%), 75-89 (4.5%), and ≥90 years (6.3%). The best area under the receiver operating characteristic curve was obtained by Light Gradient Boosting Machine model for age groups 60-74, 75-89, and ≥90 years, which were 0.892 (95% CI, 0.870-0.916), 0.886 (95% CI, 0.861-0.911), and 0.838 (95% CI, 0.782-0.887), respectively. Our study showed that EDLOS and BT were statistically correlated with IHM (p < 0.001), and a significantly higher risk of IHM was found in low EDLOS and high BT. The flagged rate of quality assurance issues was higher in lower EDLOS ≤1 h (9.96%) vs. higher EDLOS 7 h <t≤ 8 h (1.84%). Special attention should be given to patients admitted after a short stay in the ED and a long BT, and new concepts of ED care processes including specific areas and teams dedicated to older patients care could be proposed to policymakers.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/jcm12144750
https://hdl.handle.net/10459.1/464303
url https://doi.org/10.3390/jcm12144750
https://hdl.handle.net/10459.1/464303
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/jcm12144750
Journal of Clinical Medicine, 2023, vol. 12, núm. 14
dc.rights.none.fl_str_mv cc-by (c)Authors, 2023
Attribution 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c)Authors, 2023
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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