Why isn’t frailty being assessed on an ongoing basis within the cardiac setting?
Notice bibliographique
Résumé
This invited commentary refers to ‘Association between walking speed early after admission and all-cause death and/or re-admission in patients with acute decompensated heart failure,’ by K. Nozaki et al. https://doi.org/10.1093/eurjcn/zvad092 What is it about screening and assessing for frailty that scares us? Is it that we don’t know how to do it? Or that we just don’t feel it is a relevant part of the care we should be providing to our clients over the age of 65? In an article published in the European Journal of Cardiovascular Nursing, the authors investigated the associations between walking speed early after admission and clinical events in patients with acute decompensated heart failure (ADHF).1 They found faster walking speed within 4 days after admission was associated with favourable clinical outcomes in patients with ADHF. The results suggest that measuring walking speed in acute phase is useful for earlier risk stratification. The authors suggest that frailty is a potential mechanism of action that may account for walking speed. This is indeed a correct assumption, as there have been significant evidence that, as the authors have rightly stated, indicate decreased walking speed is a typical index of frailty, and frailty as a chronic condition has been associated with poor prognosis in specific populations. However, within Nozaki et al.’s study,1 frailty data were not analysed. Thus, this begs the question that if the authors knew of the impact of frailty, then why were frailty outcomes not assessed? In fact, Nozaki et al. did collect the standardized methodology for assessing walking speed to ensure reproducibility, but they did not analyse it from a frailty perspective. This standardized methodology consisted of a walking speed of ≥0.9 m/s which rules out the presence of frailty, while a walking speed of ≤0.8 m/s doubles the probability of a diagnosis of frailty. Adults over the age of 65 years diagnosed with cardiovascular disease such as heart failure (HF) usually have other chronic conditions that are responsible for major functional decline that include multimorbid conditions, polypharmacy, and geriatric syndromes inclusive of delirium, dementia, and depression. These conditions can lead to an overall state of reduced physiological reserve in multiple organ systems resulting in frailty.2 Evidence indicates early diagnosis of frailty in primary care should be an important first step when caring for patients over the age of 65 because of its high prevalence. Within the HF population, it is recommended that frailty be assessed at each stage of the HF trajectory.3 As well, potential treatments should be used to delay or even reverse frailty in its early stages.4 Thus, during any initial interactions with clients over the age of 65, clinicians should always consider the individual’s degree of frailty which can impact on walking speed and physical function such as HF outcomes. Since frailty is a predictor of adverse outcomes, inclusive of mortality, it is important to be aware of frailty when proposing treatment interventions.5 In addition to evaluating the degree of frailty, potential reversible risk factors for frailty should also be assessed. These reversible risk factors include malnutrition, dehydration, reduced daily physical activity, presence of chronic disease resulting in loss of muscle mass, dementia and/or cognitive dysfunction, lack of social supports, and loneliness.3 Finally, within the HF population, frailty can negatively impact on symptom presentation, as well as the management of disease progression.3 Wleklik et al.3 suggest when caring for frail patients living with HF an individualized approach to care should be designed that consists of strategies aimed at reversible risk factors and somatic and mental health symptoms.3 As well, the use of a comprehensive discharge plan that includes risk counselling during invasive therapeutic procedures and the delivery of care practice from a patient-centred care approach should also be considered. Collectively, these strategies should be implemented on an ongoing basis when engaging with adults over 65 years of age, who have been diagnosed with a cardiovascular disease such as HF.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,019 | 0,024 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,007 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».