Dataset Information

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 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.

SUBMITTER: Stow D

PROVIDER: S-EPMC6148780 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Stow Daniel D Matthews Fiona E FE Hanratty Barbara B

BMC medicine 20180921 1

<h4>Background</h4>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.<h4>Methods</h4>Electronic health records from 13,149 adults (cases) age 75 and over who died during a 1-year period (1 January 2015 ...[more]

PMID: 30236103

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Project description:ObjectivesTo assess the degree of frailty in older people with different advanced diseases and its relationship with end-of-life illness trajectories and survival.MethodsProspective, observational study, including all patients admitted to the Acute Geriatric Unit of the University Hospital of Vic (Spain) during 12 consecutive months (2014-2015), followed for up to 2 years. Participants were identified as end-of-life people (EOLp) using the NECPAL (NECesidades PALiativas, palliative care needs) tool and were classified according to their dominant illness trajectory. The Frail-VIG index (Valoración Integral Geriátrica, Comprehensive Geriatric Assessment) was used to quantify frailty degree, to calculate the relationship between frailty and mortality (Receiver Operating Characteristic (ROC) curves), and to assess the combined effect of frailty degree and illness trajectories on survival (Cox proportional hazards model). Survival curves were plotted using the Kaplan-Meier estimator with participants classified into four groups (ie, no frailty, mild frailty, moderate frailty and advanced frailty) and were compared using the log-rank test.ResultsOf the 590 persons with a mean (SD) age of 86.4 (5.6) years recruited, 260 (44.1%) were identified as EOLp, distributed into cancer (n=31, 11.9%), organ failure (n=79, 30.4%), dementia (n=86, 33.1%) and multimorbidity (n=64, 24.6%) trajectories. All 260 EOLp had some degree of frailty, mostly advanced frailty (n=184, 70.8%), regardless of the illness trajectory, and 220 (84.6%) died within 2 years. The area under the ROC curve (95% CI) after 2 years of follow-up for EOLp was 0.87 (0.84 to 0.92) with different patterns of survival decline in the different end-of-life trajectories (p<0.0001). Cox regression analyses showed that each additional deficit of the Frail-VIG index increased the risk of death by 61.5%, 30.1%, 29.6% and 12.9% in people with dementia, organ failure, multimorbidity and cancer, respectively (p<0.01 for all the coefficients).ConclusionsAll older people towards the end-of-life in this study were frail, mostly with advanced frailty. The degree of frailty is related to survival across the different illness trajectories despite the differing survival patterns among trajectories. Frailty indexes may be useful to assess end-of-life older people, regardless of their trajectory.

Project description:BackgroundImproved understanding of the quality of survival of patients is crucial in evaluating trauma care, understanding recovery patterns and timeframes, and informing healthcare, social, and disability service provision. We aimed to describe the longer-term health status of seriously injured patients, identify predictors of outcome, and establish recovery trajectories by population characteristics.Methods and findingsA population-based, prospective cohort study using the Victorian State Trauma Registry (VSTR) was undertaken. We followed up 2,757 adult patients, injured between July 2011 and June 2012, through deaths registry linkage and telephone interview at 6-, 12-, 24-, and 36-months postinjury. The 3-level EuroQol 5 dimensions questionnaire (EQ-5D-3L) was collected, and mixed-effects regression modelling was used to identify predictors of outcome, and recovery trajectories, for the EQ-5D-3L items and summary score. Mean (SD) age of participants was 50.8 (21.6) years, and 72% were male. Twelve percent (n = 333) died during their hospital stay, 8.1% (n = 222) of patients died postdischarge, and 155 (7.0%) were known to have survived to 36-months postinjury but were lost to follow-up at all time points. The prevalence of reporting problems at 36-months postinjury was 37% for mobility, 21% for self-care, 47% for usual activities, 50% for pain/discomfort, and 41% for anxiety/depression. Continued improvement to 36-months postinjury was only present for the usual activities item; the adjusted relative risk (ARR) of reporting problems decreased from 6 to 12 (ARR 0.87, 95% CI: 0.83-0.90), 12 to 24 (ARR 0.94, 95% CI: 0.90-0.98), and 24 to 36 months (ARR 0.95, 95% CI: 0.95-0.99). The risk of reporting problems with pain or discomfort increased from 24- to 36-months postinjury (ARR 1.06, 95% CI: 1.01, 1.12). While loss to follow-up was low, there was responder bias with patients injured in intentional events, younger, and less seriously injured patients less likely to participate; therefore, these patient subgroups were underrepresented in the study findings.ConclusionsThe prevalence of ongoing problems at 3-years postinjury is high, confirming that serious injury is frequently a chronic disorder. These findings have implications for trauma system design. Investment in interventions to reduce the longer-term impact of injuries is needed, and greater investment in primary prevention is needed.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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