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Record W3023439158 · doi:10.1093/ageing/afaa095

Frailty in the face of COVID-19

2020· article· en· W3023439158 on OpenAlexaffabout
Ruth E. Hubbard, Andrea B. Maier, Sarah N. Hilmer, Vasi Naganathan, Christopher Etherton‐Beer, Kenneth Rockwood

Bibliographic record

VenueAge and Ageing · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Face (sociological concept)BetacoronavirusPandemicPneumoniaMEDLINEVirologyInternal medicineOutbreakLinguistics

Abstract

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The Clinical Frailty Scale is a quick and reliable screening tool for frailty. While the CFS has value in allocation of scarce health resources, it also has limitations. Frailty is a continuum rather than a dichotomous variable. The type and severity of the presenting illness are important variables independently associated with the clinical outcome. A person-centred approach should consider the severity of illness and likelihood of success as well as the degree of frailty. We are living in extraordinary times and experiencing an unprecedented surge in demand for health care services. Older people are at significant increased risk from coronavirus disease (COVID-19) [1] due to decreased immune function and multi-morbidity. Data from the USA and China show people aged >65 years represent half of the admissions to hospital related to COVID-19, more than half of the admissions to the intensive care unit (ICU) and account for 80% of deaths [2]. Rapidly increasing healthcare demand due to COVID-19 requires clinicians to make difficult medical and ethical decisions about the treatment of older people, models of care and triage systems. Algorithms and scoring systems are being developed to predict risks of mortality in relation to the most limited resources such as mechanical ventilation. Screening of frailty is being proposed as a key tool to assist in this triage process [3]. Frailty has become a cornerstone of geriatric medicine and geriatricians have long advocated for screening of frailty whenever older people access health care. This is justified: frailty can capture the health status of an older person and is a predictor of multiple adverse outcomes both for community-dwellers [4] and for inpatients [5]. On this basis, geriatricians have promoted development and broad uptake of convenient screening and assessment tools to assist in the identification of people who live with varying degrees of frailty. The Clinical Frailty Scale (CFS) is a quick and reliable screening tool for frailty, which performs better than measures of cognition, function or comorbidity in assessing medium-term risk of death [6]. The CFS was developed and validated to summarise the clinical judgment of a geriatrician completing a comprehensive geriatric assessment (CGA). CGA is multidimensional process that identifies medical, social and functional needs and the CFS, even as currently employed as a screening tool, takes into account physical and cognitive function, health attitude, comorbidities and symptom management. While we agree that a multidimensional measure of frailty such as the CFS has value in allocation of scarce health resources, it is important for clinicians and administrators to understand its limitations when used in the acute hospital setting. Frailty is not synonymous with end-of-life. In a non-COVID-19 related study of 15,613 patients aged ≥80 years in ICUs across Australia, those with a CFS ≥ 5 had significantly poorer health outcomes than age matched peers who were more robust, but the prevalence of in-hospital mortality (17.6 versus 8.2%) and of new discharges to residential aged care facilities (4.9 versus 2.8%) suggest the majority of frail patients do survive and return home to the community [7]. To the best of our knowledge, appropriate cutpoints for the use of frailty scales to determine access of older people to health care have not been studied. In the UK, National Institute for Health & Care Excellence (NICE) guidelines suggest that COVID positive patients with a CFS ≥ 5 would not benefit from admission to ICU [3], yet frailty is not a dichotomous variable. Pre-COVID studies report a gradation in outcomes across CFS categories [6]; older people with a CFS of 5 (limited dependence on others for instrumental activities of daily living) differ significantly from those with a CFS of 8 (completely dependent for all personal care) not just in functional status but in their ability to recover from any insults. Most importantly, the type and severity of the presenting illness are important variables independently associated with the clinical outcome. Acute illness is less well tolerated in frailer patients, but the degree of illness acuity and the degree of frailty are each important [8]. There are other mediating factors: female sex [9], smoking [10] and social vulnerability [11] also influence how risk is expressed in relation to frailty. Across grades of frailty, men, smokers and people who are more socially vulnerable have poorer outcomes. In the acute instance, these factors are no more remediable than is illness acuity, but it does draw to attention that even a fair, non–age-based assessment can still be biased. In summary, we recommend against the use of screening tools (including the CFS when used as such) as the sole component to ration access of older people to health care. Instead we recommend that frailty screening tools are implemented as a rapid component of a person-centred approach to assessment that takes account of three key biomedical factors: severity of the presenting acute illness, the likelihood of medical interventions being successful and the degree of frailty. Through Dalhousie University, Ken Rockwood has asserted copyright of the Clinical Frailty Scale. It is free for research, education, and not-for-profit health care. Users are asked to indicate that they will not change or commercialize it. None.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.123

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.320
Teacher spread0.253 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations94
Published2020
Admission routes2
Has abstractyes

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