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Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours
PURPOSE: Over coming decades, a rise in the number of short, acute hospitalizations of older people is to be expected. To help physicians identify high-risk patients prior to discharge, we aimed to develop a model capable of predicting the risk of 30-day mortality for older patients discharged from...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10264096/ https://www.ncbi.nlm.nih.gov/pubmed/37324726 http://dx.doi.org/10.2147/CLEP.S405485 |
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author | Heltø, Amalia Lærke Kjær Rosager, Emilie Vangsgaard Aasbrenn, Martin Maule, Cathrine Fox Petersen, Janne Nielsen, Finn Erland Suetta, Charlotte Gregersen, Rasmus |
author_facet | Heltø, Amalia Lærke Kjær Rosager, Emilie Vangsgaard Aasbrenn, Martin Maule, Cathrine Fox Petersen, Janne Nielsen, Finn Erland Suetta, Charlotte Gregersen, Rasmus |
author_sort | Heltø, Amalia Lærke Kjær |
collection | PubMed |
description | PURPOSE: Over coming decades, a rise in the number of short, acute hospitalizations of older people is to be expected. To help physicians identify high-risk patients prior to discharge, we aimed to develop a model capable of predicting the risk of 30-day mortality for older patients discharged from short, acute hospitalizations and to examine how model performance changed with an increasing amount of information. METHODS: This registry-based study included acute hospitalizations in Denmark for 2016–2018 lasting ≤24 hours where patients were permanent residents, ≥65 years old, and discharged alive. Utilizing many different predictor variables, we developed random forest models with an increasing amount of information, compared their performance, and examined important variables. RESULTS: We included 107,132 patients with a median age of 75 years. Of these, 3.3% (n=3575) died within 30 days of discharge. Model performance improved especially with the addition of laboratory results and information on prior acute admissions (AUROC 0.835), and again with comorbidities and number of prescription drugs (AUROC 0.860). Model performance did not improve with the addition of sociodemographic variables (AUROC 0.861), apart from age and sex. Important variables included age, dementia, number of prescription drugs, C-reactive protein, and eGFR. CONCLUSION: The best model accurately estimated the risk of short-term mortality for older patients following short, acute hospitalizations. Trained on a large and heterogeneous dataset, the model is applicable to most acute clinical settings and could be a useful tool for physicians prior to discharge. |
format | Online Article Text |
id | pubmed-10264096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-102640962023-06-15 Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours Heltø, Amalia Lærke Kjær Rosager, Emilie Vangsgaard Aasbrenn, Martin Maule, Cathrine Fox Petersen, Janne Nielsen, Finn Erland Suetta, Charlotte Gregersen, Rasmus Clin Epidemiol Original Research PURPOSE: Over coming decades, a rise in the number of short, acute hospitalizations of older people is to be expected. To help physicians identify high-risk patients prior to discharge, we aimed to develop a model capable of predicting the risk of 30-day mortality for older patients discharged from short, acute hospitalizations and to examine how model performance changed with an increasing amount of information. METHODS: This registry-based study included acute hospitalizations in Denmark for 2016–2018 lasting ≤24 hours where patients were permanent residents, ≥65 years old, and discharged alive. Utilizing many different predictor variables, we developed random forest models with an increasing amount of information, compared their performance, and examined important variables. RESULTS: We included 107,132 patients with a median age of 75 years. Of these, 3.3% (n=3575) died within 30 days of discharge. Model performance improved especially with the addition of laboratory results and information on prior acute admissions (AUROC 0.835), and again with comorbidities and number of prescription drugs (AUROC 0.860). Model performance did not improve with the addition of sociodemographic variables (AUROC 0.861), apart from age and sex. Important variables included age, dementia, number of prescription drugs, C-reactive protein, and eGFR. CONCLUSION: The best model accurately estimated the risk of short-term mortality for older patients following short, acute hospitalizations. Trained on a large and heterogeneous dataset, the model is applicable to most acute clinical settings and could be a useful tool for physicians prior to discharge. Dove 2023-06-12 /pmc/articles/PMC10264096/ /pubmed/37324726 http://dx.doi.org/10.2147/CLEP.S405485 Text en © 2023 Heltø et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Heltø, Amalia Lærke Kjær Rosager, Emilie Vangsgaard Aasbrenn, Martin Maule, Cathrine Fox Petersen, Janne Nielsen, Finn Erland Suetta, Charlotte Gregersen, Rasmus Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours |
title | Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours |
title_full | Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours |
title_fullStr | Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours |
title_full_unstemmed | Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours |
title_short | Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours |
title_sort | predicting short-term mortality in older patients discharged from acute hospitalizations lasting less than 24 hours |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10264096/ https://www.ncbi.nlm.nih.gov/pubmed/37324726 http://dx.doi.org/10.2147/CLEP.S405485 |
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