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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381297/ https://www.ncbi.nlm.nih.gov/pubmed/37510865 http://dx.doi.org/10.3390/jcm12144750 |
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author | Wu, Lijuan Chen, Xuanhui Khalemsky, Anna Li, Deyang Zoubeidi, Taoufik Lauque, Dominique Alsabri, Mohammed Boudi, Zoubir Kumar, Vijaya Arun Paxton, James Tsilimingras, Dionyssios Kurland, Lisa Schwartz, David Hachimi-Idrissi, Said Camargo, Carlos A. Liu, Shan W. Savioli, Gabriele Intas, Geroge Soni, Kapil Dev Junhasavasdikul, Detajin Cabello, Jose Javier Trujillano 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_facet | Wu, Lijuan Chen, Xuanhui Khalemsky, Anna Li, Deyang Zoubeidi, Taoufik Lauque, Dominique Alsabri, Mohammed Boudi, Zoubir Kumar, Vijaya Arun Paxton, James Tsilimingras, Dionyssios Kurland, Lisa Schwartz, David Hachimi-Idrissi, Said Camargo, Carlos A. Liu, Shan W. Savioli, Gabriele Intas, Geroge Soni, Kapil Dev Junhasavasdikul, Detajin Cabello, Jose Javier Trujillano 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_sort | Wu, Lijuan |
collection | PubMed |
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 [Formula: see text]) 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 [Formula: see text] h (9.96%) vs. higher EDLOS 7 h [Formula: see text] 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. |
format | Online Article Text |
id | pubmed-10381297 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103812972023-07-29 The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study Wu, Lijuan Chen, Xuanhui Khalemsky, Anna Li, Deyang Zoubeidi, Taoufik Lauque, Dominique Alsabri, Mohammed Boudi, Zoubir Kumar, Vijaya Arun Paxton, James Tsilimingras, Dionyssios Kurland, Lisa Schwartz, David Hachimi-Idrissi, Said Camargo, Carlos A. Liu, Shan W. Savioli, Gabriele Intas, Geroge Soni, Kapil Dev Junhasavasdikul, Detajin Cabello, Jose Javier Trujillano 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 J Clin Med Article 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 [Formula: see text]) 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 [Formula: see text] h (9.96%) vs. higher EDLOS 7 h [Formula: see text] 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. MDPI 2023-07-18 /pmc/articles/PMC10381297/ /pubmed/37510865 http://dx.doi.org/10.3390/jcm12144750 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Lijuan Chen, Xuanhui Khalemsky, Anna Li, Deyang Zoubeidi, Taoufik Lauque, Dominique Alsabri, Mohammed Boudi, Zoubir Kumar, Vijaya Arun Paxton, James Tsilimingras, Dionyssios Kurland, Lisa Schwartz, David Hachimi-Idrissi, Said Camargo, Carlos A. Liu, Shan W. Savioli, Gabriele Intas, Geroge Soni, Kapil Dev Junhasavasdikul, Detajin Cabello, Jose Javier Trujillano 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 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 |
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_short | The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study |
title_sort | association between emergency department length of stay and in-hospital mortality in older patients using machine learning: an observational cohort study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381297/ https://www.ncbi.nlm.nih.gov/pubmed/37510865 http://dx.doi.org/10.3390/jcm12144750 |
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