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Development and validation of retrospective electronic frailty index using operational data of aged care homes

BACKGROUND: Although elderly population is generally frail, it is important to closely monitor their health deterioration to improve the care and support in residential aged care homes (RACs). Currently, the best identification approach is through time-consuming regular geriatric assessments. This s...

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Autores principales: Sarwar, Tabinda, Jimeno Yepes, Antonio Jose, Zhang, Xiuzhen, Chan, Jeffrey, Hudson, Irene, Evans, Sarah, Cavedon, Lawrence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714152/
https://www.ncbi.nlm.nih.gov/pubmed/36451137
http://dx.doi.org/10.1186/s12877-022-03616-0
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author Sarwar, Tabinda
Jimeno Yepes, Antonio Jose
Zhang, Xiuzhen
Chan, Jeffrey
Hudson, Irene
Evans, Sarah
Cavedon, Lawrence
author_facet Sarwar, Tabinda
Jimeno Yepes, Antonio Jose
Zhang, Xiuzhen
Chan, Jeffrey
Hudson, Irene
Evans, Sarah
Cavedon, Lawrence
author_sort Sarwar, Tabinda
collection PubMed
description BACKGROUND: Although elderly population is generally frail, it is important to closely monitor their health deterioration to improve the care and support in residential aged care homes (RACs). Currently, the best identification approach is through time-consuming regular geriatric assessments. This study aimed to develop and validate a retrospective electronic frailty index (reFI) to track the health status of people staying at RACs using the daily routine operational data records. METHODS: We have access to patient records from the Royal Freemasons Benevolent Institution RACs (Australia) over the age of 65, spanning 2010 to 2021. The reFI was developed using the cumulative deficit frailty model whose value was calculated as the ratio of number of present frailty deficits to the total possible frailty indicators (32). Frailty categories were defined using population quartiles. 1, 3 and 5-year mortality were used for validation. Survival analysis was performed using Kaplan-Meier estimate. Hazard ratios (HRs) were estimated using Cox regression analyses and the association was assessed using receiver operating characteristic (ROC) curves. RESULTS: Two thousand five hundred eighty-eight residents were assessed, with an average length of stay of 1.2 ± 2.2 years. The RAC cohort was generally frail with an average reFI of 0.21 ± 0.11. According to the Kaplan-Meier estimate, survival varied significantly across different frailty categories (p < 0.01). The estimated hazard ratios (HRs) were 1.12 (95% CI 1.09–1.15), 1.11 (95% CI 1.07–1.14), and 1.1 (95% CI 1.04–1.17) at 1, 3 and 5 years. The ROC analysis of the reFI for mortality outcome showed an area under the curve (AUC) of ≥0.60 for 1, 3 and 5-year mortality. CONCLUSION: A novel reFI was developed using the routine data recorded at RACs. reFI can identify changes in the frailty index over time for elderly people, that could potentially help in creating personalised care plans for addressing their health deterioration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-03616-0.
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spelling pubmed-97141522022-12-02 Development and validation of retrospective electronic frailty index using operational data of aged care homes Sarwar, Tabinda Jimeno Yepes, Antonio Jose Zhang, Xiuzhen Chan, Jeffrey Hudson, Irene Evans, Sarah Cavedon, Lawrence BMC Geriatr Research BACKGROUND: Although elderly population is generally frail, it is important to closely monitor their health deterioration to improve the care and support in residential aged care homes (RACs). Currently, the best identification approach is through time-consuming regular geriatric assessments. This study aimed to develop and validate a retrospective electronic frailty index (reFI) to track the health status of people staying at RACs using the daily routine operational data records. METHODS: We have access to patient records from the Royal Freemasons Benevolent Institution RACs (Australia) over the age of 65, spanning 2010 to 2021. The reFI was developed using the cumulative deficit frailty model whose value was calculated as the ratio of number of present frailty deficits to the total possible frailty indicators (32). Frailty categories were defined using population quartiles. 1, 3 and 5-year mortality were used for validation. Survival analysis was performed using Kaplan-Meier estimate. Hazard ratios (HRs) were estimated using Cox regression analyses and the association was assessed using receiver operating characteristic (ROC) curves. RESULTS: Two thousand five hundred eighty-eight residents were assessed, with an average length of stay of 1.2 ± 2.2 years. The RAC cohort was generally frail with an average reFI of 0.21 ± 0.11. According to the Kaplan-Meier estimate, survival varied significantly across different frailty categories (p < 0.01). The estimated hazard ratios (HRs) were 1.12 (95% CI 1.09–1.15), 1.11 (95% CI 1.07–1.14), and 1.1 (95% CI 1.04–1.17) at 1, 3 and 5 years. The ROC analysis of the reFI for mortality outcome showed an area under the curve (AUC) of ≥0.60 for 1, 3 and 5-year mortality. CONCLUSION: A novel reFI was developed using the routine data recorded at RACs. reFI can identify changes in the frailty index over time for elderly people, that could potentially help in creating personalised care plans for addressing their health deterioration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-03616-0. BioMed Central 2022-12-01 /pmc/articles/PMC9714152/ /pubmed/36451137 http://dx.doi.org/10.1186/s12877-022-03616-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sarwar, Tabinda
Jimeno Yepes, Antonio Jose
Zhang, Xiuzhen
Chan, Jeffrey
Hudson, Irene
Evans, Sarah
Cavedon, Lawrence
Development and validation of retrospective electronic frailty index using operational data of aged care homes
title Development and validation of retrospective electronic frailty index using operational data of aged care homes
title_full Development and validation of retrospective electronic frailty index using operational data of aged care homes
title_fullStr Development and validation of retrospective electronic frailty index using operational data of aged care homes
title_full_unstemmed Development and validation of retrospective electronic frailty index using operational data of aged care homes
title_short Development and validation of retrospective electronic frailty index using operational data of aged care homes
title_sort development and validation of retrospective electronic frailty index using operational data of aged care homes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714152/
https://www.ncbi.nlm.nih.gov/pubmed/36451137
http://dx.doi.org/10.1186/s12877-022-03616-0
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