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Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia
INTRODUCTION: Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. METHODS: We ident...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520763/ https://www.ncbi.nlm.nih.gov/pubmed/36204350 http://dx.doi.org/10.1002/trc2.12351 |
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author | Wang, Xin Ezeana, Chika F. Wang, Lin Puppala, Mamta Huang, Yan‐Siang He, Yunjie Yu, Xiaohui Yin, Zheng Zhao, Hong Lai, Eugene C. Wong, Stephen T. C. |
author_facet | Wang, Xin Ezeana, Chika F. Wang, Lin Puppala, Mamta Huang, Yan‐Siang He, Yunjie Yu, Xiaohui Yin, Zheng Zhao, Hong Lai, Eugene C. Wong, Stephen T. C. |
author_sort | Wang, Xin |
collection | PubMed |
description | INTRODUCTION: Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. METHODS: We identified risk factors, that is, demographics, hospital complications, pre‐admission, and post‐admission risk factors including medical history and comorbidities, affecting hospitalization outcomes determined by hospital stays and discharge dispositions. Over 150 clinical and demographic factors of 15,678 encounters (8407 patients) were retrieved from our institution's data warehouse. We further narrowed them down to twenty factors through feature selection engineering by using analysis of variance (ANOVA) and Glmnet. We developed an explainable machine‐learning model to predict hospitalization outcomes among geriatric patients with dementia. RESULTS: Our model is based on stacking ensemble learning and achieved accuracy of 95.6% and area under the curve (AUC) of 0.757. It outperformed prevalent methods of risk assessment for encounters of patients with Alzheimer's disease dementia (ADD) (4993), vascular dementia (VD) (4173), Parkinson's disease with dementia (PDD) (3735), and other unspecified dementias (OUD) (2777). Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi‐dementia groups. DISCUSSION: Our model identified several predictive factors that can be modified or intervened so that efforts can be made to prevent recurrence or mitigate their adverse effects. Knowledge of the modifiable risk factors would help guide early interventions for patients at high risk for poor hospitalization outcome as defined by hospital stays longer than seven days, undesirable discharge disposition, or both. The interventions include starting specific protocols on modifiable risk factors like encephalopathy, falls, and infections, where non‐existent or not routine, to improve hospitalization outcomes of geriatric patients with dementia. HIGHLIGHTS: A total 15,678 encounters of Geriatrics with dementia with a final 20 risk factors. Developed a predictive model for hospitalization outcomes for multi‐dementia types. Risk factors for each type were identified including those amenable to interventions. Top factors are encephalopathy, pressure ulcers, urinary tract infection (UTI), falls, and admission source. With accuracy of 95.6%, our ensemble predictive model outperforms other models. |
format | Online Article Text |
id | pubmed-9520763 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95207632022-10-05 Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia Wang, Xin Ezeana, Chika F. Wang, Lin Puppala, Mamta Huang, Yan‐Siang He, Yunjie Yu, Xiaohui Yin, Zheng Zhao, Hong Lai, Eugene C. Wong, Stephen T. C. Alzheimers Dement (N Y) Research Articles INTRODUCTION: Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. METHODS: We identified risk factors, that is, demographics, hospital complications, pre‐admission, and post‐admission risk factors including medical history and comorbidities, affecting hospitalization outcomes determined by hospital stays and discharge dispositions. Over 150 clinical and demographic factors of 15,678 encounters (8407 patients) were retrieved from our institution's data warehouse. We further narrowed them down to twenty factors through feature selection engineering by using analysis of variance (ANOVA) and Glmnet. We developed an explainable machine‐learning model to predict hospitalization outcomes among geriatric patients with dementia. RESULTS: Our model is based on stacking ensemble learning and achieved accuracy of 95.6% and area under the curve (AUC) of 0.757. It outperformed prevalent methods of risk assessment for encounters of patients with Alzheimer's disease dementia (ADD) (4993), vascular dementia (VD) (4173), Parkinson's disease with dementia (PDD) (3735), and other unspecified dementias (OUD) (2777). Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi‐dementia groups. DISCUSSION: Our model identified several predictive factors that can be modified or intervened so that efforts can be made to prevent recurrence or mitigate their adverse effects. Knowledge of the modifiable risk factors would help guide early interventions for patients at high risk for poor hospitalization outcome as defined by hospital stays longer than seven days, undesirable discharge disposition, or both. The interventions include starting specific protocols on modifiable risk factors like encephalopathy, falls, and infections, where non‐existent or not routine, to improve hospitalization outcomes of geriatric patients with dementia. HIGHLIGHTS: A total 15,678 encounters of Geriatrics with dementia with a final 20 risk factors. Developed a predictive model for hospitalization outcomes for multi‐dementia types. Risk factors for each type were identified including those amenable to interventions. Top factors are encephalopathy, pressure ulcers, urinary tract infection (UTI), falls, and admission source. With accuracy of 95.6%, our ensemble predictive model outperforms other models. John Wiley and Sons Inc. 2022-09-29 /pmc/articles/PMC9520763/ /pubmed/36204350 http://dx.doi.org/10.1002/trc2.12351 Text en © 2022 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Articles Wang, Xin Ezeana, Chika F. Wang, Lin Puppala, Mamta Huang, Yan‐Siang He, Yunjie Yu, Xiaohui Yin, Zheng Zhao, Hong Lai, Eugene C. Wong, Stephen T. C. Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia |
title | Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia |
title_full | Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia |
title_fullStr | Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia |
title_full_unstemmed | Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia |
title_short | Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia |
title_sort | risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520763/ https://www.ncbi.nlm.nih.gov/pubmed/36204350 http://dx.doi.org/10.1002/trc2.12351 |
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