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Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders

BACKGROUND: Early recognition of older people at risk of undesirable clinical outcomes is vital in preventing future disabling conditions. Here, we report the prognostic performance of an electronic frailty index (eFI) in comparison with traditional tools among nonfrail and prefrail community-dwelli...

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Autores principales: Lin, Kun-Pei, Li, Hsin-Yi, Chen, Jen-Hau, Lu, Feng-Ping, Wen, Chiung-Jung, Chou, Yi-Chun, Wu, Meng-Chen, (Derrick) Chan, Ding-Cheng, Chen, Yung-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408173/
https://www.ncbi.nlm.nih.gov/pubmed/37550602
http://dx.doi.org/10.1186/s12877-023-04160-1
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author Lin, Kun-Pei
Li, Hsin-Yi
Chen, Jen-Hau
Lu, Feng-Ping
Wen, Chiung-Jung
Chou, Yi-Chun
Wu, Meng-Chen
(Derrick) Chan, Ding-Cheng
Chen, Yung-Ming
author_facet Lin, Kun-Pei
Li, Hsin-Yi
Chen, Jen-Hau
Lu, Feng-Ping
Wen, Chiung-Jung
Chou, Yi-Chun
Wu, Meng-Chen
(Derrick) Chan, Ding-Cheng
Chen, Yung-Ming
author_sort Lin, Kun-Pei
collection PubMed
description BACKGROUND: Early recognition of older people at risk of undesirable clinical outcomes is vital in preventing future disabling conditions. Here, we report the prognostic performance of an electronic frailty index (eFI) in comparison with traditional tools among nonfrail and prefrail community-dwelling older adults. The study is to investigate the predictive utility of a deficit-accumulation eFI in community elders without overt frailty. METHODS: Participants aged 65–80 years with a Clinical Frailty Scale of 1–3 points were recruited and followed for 2 years. The eFI score and Fried’s frailty scale were determined by using a semiautomated platform of self-reported questionnaires and objective measurements which yielded cumulative deficits and physical phenotypes from 80 items of risk variables. Kaplan–Meier method and Cox proportional hazards regression were used to analyze the severity of frailty in relation to adverse outcomes of falls, emergency room (ER) visits and hospitalizations during 2 years’ follow-up. RESULTS: A total of 427 older adults were evaluated and dichotomized by the median FI score. Two hundred and sixty (60.9%) and 167 (39.1%) elders were stratified into the low- (eFI ≤ 0.075) and the high-risk (eFI > 0.075) groups, respectively. During the follow-up, 77 (47.0%) individuals developed adverse events in the high-risk group, compared with 79 (30.5%) in the low-risk group (x(2), p = 0.0006). In multivariable models adjusted for age and sex, the increased risk of all three events combined in the high- vs. low-risk group remained significant (adjusted hazard ratio (aHR) = 3.08, 95% confidence interval (CI): 1.87–5.07). For individual adverse event, the aHRs were 2.20 (CI: 1.44–3.36) for falls; 1.67 (CI: 1.03–2.70) for ER visits; and 2.84 (CI: 1.73–4.67) for hospitalizations. Compared with the traditional tools, the eFI stratification (high- vs. low-risk) showed better predictive performance than either CFS rating (managing well vs. fit to very fit; not discriminative in hospitalizations) or Fried’s scale (prefrail to frail vs. nonfrail; not discriminative in ER visits). CONCLUSION: The eFI system is a useful frailty tool which effectively predicts the risk of adverse healthcare outcomes in nonfrail and/or prefrail older adults over a period of 2 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04160-1.
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spelling pubmed-104081732023-08-09 Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders Lin, Kun-Pei Li, Hsin-Yi Chen, Jen-Hau Lu, Feng-Ping Wen, Chiung-Jung Chou, Yi-Chun Wu, Meng-Chen (Derrick) Chan, Ding-Cheng Chen, Yung-Ming BMC Geriatr Research BACKGROUND: Early recognition of older people at risk of undesirable clinical outcomes is vital in preventing future disabling conditions. Here, we report the prognostic performance of an electronic frailty index (eFI) in comparison with traditional tools among nonfrail and prefrail community-dwelling older adults. The study is to investigate the predictive utility of a deficit-accumulation eFI in community elders without overt frailty. METHODS: Participants aged 65–80 years with a Clinical Frailty Scale of 1–3 points were recruited and followed for 2 years. The eFI score and Fried’s frailty scale were determined by using a semiautomated platform of self-reported questionnaires and objective measurements which yielded cumulative deficits and physical phenotypes from 80 items of risk variables. Kaplan–Meier method and Cox proportional hazards regression were used to analyze the severity of frailty in relation to adverse outcomes of falls, emergency room (ER) visits and hospitalizations during 2 years’ follow-up. RESULTS: A total of 427 older adults were evaluated and dichotomized by the median FI score. Two hundred and sixty (60.9%) and 167 (39.1%) elders were stratified into the low- (eFI ≤ 0.075) and the high-risk (eFI > 0.075) groups, respectively. During the follow-up, 77 (47.0%) individuals developed adverse events in the high-risk group, compared with 79 (30.5%) in the low-risk group (x(2), p = 0.0006). In multivariable models adjusted for age and sex, the increased risk of all three events combined in the high- vs. low-risk group remained significant (adjusted hazard ratio (aHR) = 3.08, 95% confidence interval (CI): 1.87–5.07). For individual adverse event, the aHRs were 2.20 (CI: 1.44–3.36) for falls; 1.67 (CI: 1.03–2.70) for ER visits; and 2.84 (CI: 1.73–4.67) for hospitalizations. Compared with the traditional tools, the eFI stratification (high- vs. low-risk) showed better predictive performance than either CFS rating (managing well vs. fit to very fit; not discriminative in hospitalizations) or Fried’s scale (prefrail to frail vs. nonfrail; not discriminative in ER visits). CONCLUSION: The eFI system is a useful frailty tool which effectively predicts the risk of adverse healthcare outcomes in nonfrail and/or prefrail older adults over a period of 2 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04160-1. BioMed Central 2023-08-07 /pmc/articles/PMC10408173/ /pubmed/37550602 http://dx.doi.org/10.1186/s12877-023-04160-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Lin, Kun-Pei
Li, Hsin-Yi
Chen, Jen-Hau
Lu, Feng-Ping
Wen, Chiung-Jung
Chou, Yi-Chun
Wu, Meng-Chen
(Derrick) Chan, Ding-Cheng
Chen, Yung-Ming
Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders
title Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders
title_full Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders
title_fullStr Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders
title_full_unstemmed Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders
title_short Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders
title_sort prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408173/
https://www.ncbi.nlm.nih.gov/pubmed/37550602
http://dx.doi.org/10.1186/s12877-023-04160-1
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