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Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis

INTRODUCTION: Delirium is an acute and fluctuating change in attention and cognition that increases the risk of functional decline, institutionalisation and death in hospitalised patients. After delirium, patients have a significantly higher risk of readmission to hospital. Our aim was to investigat...

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Autores principales: Friedrich, Michaela‐Elena, Perera, Gayan, Leutgeb, Lisa, Haardt, David, Frey, Richard, Stewart, Robert, Mueller, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463092/
https://www.ncbi.nlm.nih.gov/pubmed/36441117
http://dx.doi.org/10.1111/acps.13523
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author Friedrich, Michaela‐Elena
Perera, Gayan
Leutgeb, Lisa
Haardt, David
Frey, Richard
Stewart, Robert
Mueller, Christoph
author_facet Friedrich, Michaela‐Elena
Perera, Gayan
Leutgeb, Lisa
Haardt, David
Frey, Richard
Stewart, Robert
Mueller, Christoph
author_sort Friedrich, Michaela‐Elena
collection PubMed
description INTRODUCTION: Delirium is an acute and fluctuating change in attention and cognition that increases the risk of functional decline, institutionalisation and death in hospitalised patients. After delirium, patients have a significantly higher risk of readmission to hospital. Our aim was to investigate factors associated with hospital readmission in people with delirium. METHODS: We carried out an observational retrospective cohort study using linked mental health care and hospitalisation records from South London. Logistic regression models were used to predict the odds of 30‐day readmission and Cox proportional hazard models to calculate readmission risks when not restricting follow‐up time. RESULTS: Of 2814 patients (mean age 78.9 years SD ±11.8) discharged from hospital after an episode of delirium, 823 (29.3%) were readmitted within 30 days. Depressed mood (odds ratio (OR) 1.34 (95% confidence interval (CI) 1.08–1.66)), moderate‐to‐severe physical health problems (OR 1.67 (95% CI 1.18–2.2.36)) and a history of serious circulatory disease (OR 1.29 (95% CI 1.07–1.55)) were associated with higher odds of hospital readmission, whereas a diagnosis of delirium superimposed on dementia (OR 0.67 (95% CI 0.53–0.84)) and problematic alcohol/substance (OR 0.54 (95% CI 0.33–0.89)) use were associated with lower odds. Cox proportionate hazard models showed similar results. CONCLUSION: Almost one‐third of patients with delirium were readmitted within a short period of time, a more detailed understanding of the underlying risk factors could help prevent readmissions. Our findings indicate that the aetiology (as alcohol‐related delirium), the recognition that delirium occurred in the context of dementia, as well as potentially modifiable factors, as depressed mood affect readmission risk, and should be assessed in clinical settings.
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spelling pubmed-104630922023-08-30 Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis Friedrich, Michaela‐Elena Perera, Gayan Leutgeb, Lisa Haardt, David Frey, Richard Stewart, Robert Mueller, Christoph Acta Psychiatr Scand Original Articles INTRODUCTION: Delirium is an acute and fluctuating change in attention and cognition that increases the risk of functional decline, institutionalisation and death in hospitalised patients. After delirium, patients have a significantly higher risk of readmission to hospital. Our aim was to investigate factors associated with hospital readmission in people with delirium. METHODS: We carried out an observational retrospective cohort study using linked mental health care and hospitalisation records from South London. Logistic regression models were used to predict the odds of 30‐day readmission and Cox proportional hazard models to calculate readmission risks when not restricting follow‐up time. RESULTS: Of 2814 patients (mean age 78.9 years SD ±11.8) discharged from hospital after an episode of delirium, 823 (29.3%) were readmitted within 30 days. Depressed mood (odds ratio (OR) 1.34 (95% confidence interval (CI) 1.08–1.66)), moderate‐to‐severe physical health problems (OR 1.67 (95% CI 1.18–2.2.36)) and a history of serious circulatory disease (OR 1.29 (95% CI 1.07–1.55)) were associated with higher odds of hospital readmission, whereas a diagnosis of delirium superimposed on dementia (OR 0.67 (95% CI 0.53–0.84)) and problematic alcohol/substance (OR 0.54 (95% CI 0.33–0.89)) use were associated with lower odds. Cox proportionate hazard models showed similar results. CONCLUSION: Almost one‐third of patients with delirium were readmitted within a short period of time, a more detailed understanding of the underlying risk factors could help prevent readmissions. Our findings indicate that the aetiology (as alcohol‐related delirium), the recognition that delirium occurred in the context of dementia, as well as potentially modifiable factors, as depressed mood affect readmission risk, and should be assessed in clinical settings. John Wiley and Sons Inc. 2022-12-28 2023-05 /pmc/articles/PMC10463092/ /pubmed/36441117 http://dx.doi.org/10.1111/acps.13523 Text en © 2022 The Authors. Acta Psychiatrica Scandinavica published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Friedrich, Michaela‐Elena
Perera, Gayan
Leutgeb, Lisa
Haardt, David
Frey, Richard
Stewart, Robert
Mueller, Christoph
Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis
title Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis
title_full Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis
title_fullStr Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis
title_full_unstemmed Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis
title_short Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis
title_sort predictors of hospital readmission for patients diagnosed with delirium: an electronic health record data analysis
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463092/
https://www.ncbi.nlm.nih.gov/pubmed/36441117
http://dx.doi.org/10.1111/acps.13523
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