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Frailty trajectories to identify end of life: a longitudinal population-based study

BACKGROUND: Timely recognition of the end of life allows patients to discuss preferences and make advance plans, and clinicians to introduce appropriate care. We examined changes in frailty over 1 year, with the aim of identifying trajectories that could indicate where an individual is at increased...

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Autores principales: Stow, Daniel, Matthews, Fiona E., Hanratty, Barbara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6148780/
https://www.ncbi.nlm.nih.gov/pubmed/30236103
http://dx.doi.org/10.1186/s12916-018-1148-x
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author Stow, Daniel
Matthews, Fiona E.
Hanratty, Barbara
author_facet Stow, Daniel
Matthews, Fiona E.
Hanratty, Barbara
author_sort Stow, Daniel
collection PubMed
description BACKGROUND: Timely recognition of the end of life allows patients to discuss preferences and make advance plans, and clinicians to introduce appropriate care. We examined changes in frailty over 1 year, with the aim of identifying trajectories that could indicate where an individual is at increased risk of all-cause mortality and may require palliative care. METHODS: Electronic health records from 13,149 adults (cases) age 75 and over who died during a 1-year period (1 January 2015 to 1 January 2016) were age, sex and general practice matched to 13,149 individuals with no record of death over the same period (controls). Monthly frailty scores were obtained for 1 year prior to death for cases, and from 1 January 2015 to 1 January 2016 for controls using the electronic frailty index (eFI; a cumulative deficit measure of frailty, available in most English primary care electronic health records, and ranging in value from 0 to 1). Latent growth mixture models were used to investigate longitudinal patterns of change and associated impact on mortality. Cases were reweighted to the population level for tests of diagnostic accuracy. RESULTS: Three distinct frailty trajectories were identified. Rapidly rising frailty (initial increase of 0.022 eFI per month before slowing from a baseline eFI of 0.21) was associated with a 180% increase in mortality (OR 2.84, 95% CI 2.34–3.45) for 2.2% of the sample. Moderately increasing frailty (eFI increase of 0.007 per month, with baseline of 0.26) was associated with a 65% increase in mortality (OR 1.65, 95% CI 1.54–1.76) for 21.2% of the sample. The largest (76.6%) class was stable frailty (eFI increase of 0.001 from a baseline of 0.26). When cases were reweighted to population level, rapidly rising frailty had 99.1% specificity and 3.2% sensitivity (positive predictive value 19.8%, negative predictive value 93.3%) for predicting individual risk of mortality. CONCLUSIONS: People aged over 75 with frailty who are at highest risk of death have a distinctive frailty trajectory in the last 12 months of life, with a rapid initial rise from a low baseline, followed by a plateau. Routine measurement of frailty can be useful to support clinicians to identify people with frailty who are potential candidates for palliative care, and allow time for intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12916-018-1148-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-61487802018-09-24 Frailty trajectories to identify end of life: a longitudinal population-based study Stow, Daniel Matthews, Fiona E. Hanratty, Barbara BMC Med Research Article BACKGROUND: Timely recognition of the end of life allows patients to discuss preferences and make advance plans, and clinicians to introduce appropriate care. We examined changes in frailty over 1 year, with the aim of identifying trajectories that could indicate where an individual is at increased risk of all-cause mortality and may require palliative care. METHODS: Electronic health records from 13,149 adults (cases) age 75 and over who died during a 1-year period (1 January 2015 to 1 January 2016) were age, sex and general practice matched to 13,149 individuals with no record of death over the same period (controls). Monthly frailty scores were obtained for 1 year prior to death for cases, and from 1 January 2015 to 1 January 2016 for controls using the electronic frailty index (eFI; a cumulative deficit measure of frailty, available in most English primary care electronic health records, and ranging in value from 0 to 1). Latent growth mixture models were used to investigate longitudinal patterns of change and associated impact on mortality. Cases were reweighted to the population level for tests of diagnostic accuracy. RESULTS: Three distinct frailty trajectories were identified. Rapidly rising frailty (initial increase of 0.022 eFI per month before slowing from a baseline eFI of 0.21) was associated with a 180% increase in mortality (OR 2.84, 95% CI 2.34–3.45) for 2.2% of the sample. Moderately increasing frailty (eFI increase of 0.007 per month, with baseline of 0.26) was associated with a 65% increase in mortality (OR 1.65, 95% CI 1.54–1.76) for 21.2% of the sample. The largest (76.6%) class was stable frailty (eFI increase of 0.001 from a baseline of 0.26). When cases were reweighted to population level, rapidly rising frailty had 99.1% specificity and 3.2% sensitivity (positive predictive value 19.8%, negative predictive value 93.3%) for predicting individual risk of mortality. CONCLUSIONS: People aged over 75 with frailty who are at highest risk of death have a distinctive frailty trajectory in the last 12 months of life, with a rapid initial rise from a low baseline, followed by a plateau. Routine measurement of frailty can be useful to support clinicians to identify people with frailty who are potential candidates for palliative care, and allow time for intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12916-018-1148-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-21 /pmc/articles/PMC6148780/ /pubmed/30236103 http://dx.doi.org/10.1186/s12916-018-1148-x Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Stow, Daniel
Matthews, Fiona E.
Hanratty, Barbara
Frailty trajectories to identify end of life: a longitudinal population-based study
title Frailty trajectories to identify end of life: a longitudinal population-based study
title_full Frailty trajectories to identify end of life: a longitudinal population-based study
title_fullStr Frailty trajectories to identify end of life: a longitudinal population-based study
title_full_unstemmed Frailty trajectories to identify end of life: a longitudinal population-based study
title_short Frailty trajectories to identify end of life: a longitudinal population-based study
title_sort frailty trajectories to identify end of life: a longitudinal population-based study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6148780/
https://www.ncbi.nlm.nih.gov/pubmed/30236103
http://dx.doi.org/10.1186/s12916-018-1148-x
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