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Record W2608069909 · doi:10.1093/ageing/afx052

Acute hospital admission of the frail older person: an opportunity to discuss future care

2017· editorial· en· W2608069909 on OpenAlexaboutno aff
Lucy Pocock, Debbie Sharp

Bibliographic record

VenueAge and Ageing · 2017
Typeeditorial
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicineAcute hospitalGerontologyOlder peopleHospital admissionAcute careHealth careInternal medicine

Abstract

fetched live from OpenAlex

More than a 3rd of the UK population now lives to the age of 85 [1] and the absolute number of older adults in the population is increasing significantly [2]. Frailty is defined by Clegg et al. [3] as ‘a state of increased vulnerability to poor resolution of homoeostasis after a stressor event, which increases the risk of adverse outcomes, including falls, delirium and disability’. The prevalence increases with age, with up to half of adults over the age of 85 estimated to be frail [3, 4]. It is now well reported that increasing frailty is important as a prognostic indicator, with frailty status being strongly associated with both quality of patient outcome and mortality [5]. A recent NHS Benchmarking Network report suggests that 52% of Trusts in the UK now have a specialist frailty unit [6] and, in primary care, the new GP contract will require practices to actively identify their frail patients and review them appropriately [7]. Measurement of frailty can be performed in several ways and there are a number of tools in use. The Comprehensive Geriatric Assessment (CGA), the gold standard for the management of frailty in older people, is an holistic, multidimensional, interdisciplinary assessment of an individual and has been demonstrated to be associated with improved outcomes in a variety of settings [8]. The CGA can also be used to quantify an individual's degree of frailty in a Frailty Index (FI-CGA) [9]. In this edition of Age and Ageing Hatheway et al. present data from secondary analysis of a cohort study, initially reported in 2011, including 409 elderly patients admitted to a tertiary care teaching hospital in Canada [9]. The current study examines the relationship between the recovery of mobility and balance, initial treatment response and underlying frailty. Patients who were more frail at baseline, as reported by the patient or their family, were less likely to recover their balance and mobility (odds of no or incomplete recovery increased by 1.06 with each 0.1 increment in the FI-CGA at baseline) and this was similarly dependent on age (1.010). Improvement in mobility and balance over the first 48 h was associated with greater improvement overall and with shorter recovery times. Patients with only mild mobility impairment recovered sooner—by Day 5 about 50% with mild impairment had recovered, compared to 25% of those with moderate impairment and 10% with severe impairment. Declining mobility in the first 48 h represented a relative risk of death of 17.1. The relationship between degree of baseline frailty and recovery was independent of the extent of mobility impairment at admission. This study has immediate clinical relevance and contributes to the growing evidence base on this important topic. Although secondary analysis of existing data can be problematic, as the available data are not collected to address the particular research question, this study makes appropriate use of routinely collected data with a reasonable sample size. To improve generalisability, it would be helpful to see the analysis of data from a number of hospital sites. Almost two-thirds of people in the UK, aged over 85, die during a hospital admission [10]. The likelihood of dying in the 12 months following a hospital admission increases with age [11], however, it is difficult to predict which older patients are at risk of dying during, or soon after, a hospital admission. This study offers one approach to assessing and managing the frail older patient with acute illness to appropriately tailor their ongoing care. If we are able to identify the patients who are most likely to recover, we can target them for intensive rehabilitation and early discharge planning. Equally, those patients who are at greater risk of dying can be offered the opportunity to have advance care planning discussions and palliative care input, ensuring that unnecessary interventions are minimised. Frailty is recognized as an increasing challenge in the care of older people. This study offers a simple approach to assessing the likelihood of recovery of frail older people who are acutely unwell. Identification of patients who are less likely to recover will allow appropriate care planning decisions to be made.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.048
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.291
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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