MétaCan
Menu
Retour à la cohorte
Enregistrement W3023439158 · doi:10.1093/ageing/afaa095

Frailty in the face of COVID-19

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

Notice bibliographique

RevueAge and Ageing · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensNova Scotia Health AuthorityDalhousie University
Organismes subventionnairesnon disponible
Mots-clésMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Face (sociological concept)BetacoronavirusPandemicPneumoniaMEDLINEVirologyInternal medicineOutbreakLinguistics

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,552
Score d'incertitude au seuil0,123

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,067
Tête enseignante GPT0,320
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations94
Publié2020
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueAge and AgeingMême sujetFrailty in Older AdultsTravaux en français237 207