Frailty trajectories to identify end of life: a longitudinal population-based study
Abstract 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 in...
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12916-018-1148-x |
| _version_ | 1818337396360478720 |
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| author | Daniel Stow Fiona E. Matthews Barbara Hanratty |
| author_facet | Daniel Stow Fiona E. Matthews Barbara Hanratty |
| author_sort | Daniel Stow |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-13T14:54:33Z |
| format | Article |
| id | doaj.art-f69eb5bc218a452aa4f18ab0e77b2f9c |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-12-13T14:54:33Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-f69eb5bc218a452aa4f18ab0e77b2f9c2022-12-21T23:41:16ZengBMCBMC Medicine1741-70152018-09-011611710.1186/s12916-018-1148-xFrailty trajectories to identify end of life: a longitudinal population-based studyDaniel Stow0Fiona E. Matthews1Barbara Hanratty2Institute of Health and Society, Newcastle UniversityInstitute of Health and Society, Newcastle UniversityInstitute of Health and Society, Newcastle UniversityAbstract 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.http://link.springer.com/article/10.1186/s12916-018-1148-xFrailtyGeriatricsPalliative carePrimary careEnd of life |
| spellingShingle | Daniel Stow Fiona E. Matthews Barbara Hanratty Frailty trajectories to identify end of life: a longitudinal population-based study BMC Medicine Frailty Geriatrics Palliative care Primary care End of life |
| 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 | Frailty Geriatrics Palliative care Primary care End of life |
| url | http://link.springer.com/article/10.1186/s12916-018-1148-x |
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