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Enregistrement W4322718314 · doi:10.1093/eurjpc/zwad065

The many uses of frailty assessments in cardiac rehabilitation programs

2023· letter· en· W4322718314 sur OpenAlexaff
David B. Hogan

Notice bibliographique

RevueEuropean Journal of Preventive Cardiology · 2023
Typeletter
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineRehabilitationPhysical medicine and rehabilitationPhysical therapyGerontology

Résumé

récupéré en direct d'OpenAlex

This editorial refers to ‘Association of admission frailty and frailty changes during cardiac rehabilitation with 5-year outcomes’, by J. Quach et al., https://doi.org/10.1093/eurjpc/zwad048. Later life frailty is a state of heightened vulnerability to external stressors. Frailer older persons are at increased risk for a variety of adverse outcomes such as mortality, incident or worsening of prevalent functional limitations, lower quality of life, and hospitalization. Many approaches have been proposed for its identification and/or grading. A favored methodology is the determination of an older person’s frailty index (FI), which is the ratio of deficits (symptoms, signs, disabilities, and diseases) present to the total considered. Scores range from 0 to 1 with higher scores indicating greater frailty. Since data sources vary, not every FI includes the same deficits or even number of deficits, but they produce similar results if properly constructed. What is critical is selecting enough deficits (typically 30–40) that meet certain requirements (i.e. the items must be associated with health status, have a prevalence that generally increases with age but does not saturate too early, and cover a range of systems).1 Frailty is both common among those with cardiovascular disease and a risk factor for its development.2 Frailty assessments have been used for diverse reasons in populations with cardiovascular disease such as estimating the prognosis of older patients with coronary artery disease and helping to determine the appropriateness of transcatheter aortic valve implantation for those with aortic stenosis. An emerging area of interest is the relationship between frailty and cardiac rehabilitation (CR). The recommended core components of CR programs (baseline assessment, nutritional counseling, psychosocial management, physical activity counseling, and exercise training)3 are all well-suited to the prevention or management of frailty and its manifestations such as weakness, reduced physical activity, and fatigue.4 Pre-entry frailty assessments might help in assessing the likelihood that an older person will benefit from CR5 or, more importantly, inform what modifications to the CR program should be considered so it better meets the needs of frail participants.6 Quach et al.7 in a single-site (Halifax, Nova Scotia, Canada) study of 3371 patients enrolled in a CR program between 2005 and 2015 found statistically significant associations between greater frailty severity as measured by a FI on admission to the program and shorter times to all-cause (and cardiovascular disease-related ones for the first two outcomes listed and number of hospitalizations) mortality, first hospitalization, and first emergency department (ED) visit as well as the number of hospitalizations, hospital days, and ED visits over a 5-year span. Clinically meaningful FI improvements were seen in about a third of participants who completed the CR program. These improvements had a statistically significant effect on prolonging the time to the first all-cause hospitalization. While no other statistically significant associations between FI change during program participation and 5-year outcomes were found, these results suggest that reducing frailty might be an additional positive impact of CR programs that could lead to health care system as well as personal benefits. To provide context in determining if the results are applicable to your setting, the study population had a mean age of 61.9 years, 74.2% were males, and a bit more than half had an admitting diagnosis of either coronary artery disease (26.7%) or myocardial infarction (28.3%). The CR program was 12 weeks in duration. It offered three group-based approximately 60-min sessions per week consisting of exercise (twice weekly) and education (once weekly) components. Attendance at scheduled sessions was nearly 80%. The FI used in the study consisted of 25 items routinely collected by the CR program and stored on their database. The domains covered included cardiovascular risk factors, cardiovascular symptoms, cardiovascular fitness, self-reported quality of life based on components of the SF-36, body composition, and diet quality. A FI was calculated on all patients who had less than 30% (or 8/25) of items missing. This led to a ‘lean’ FI, especially among those with missing data. Also, it should be noted that the FI used in this study would likely not be transferable to another CR program because of variability in the data collected and stored by individual programs. Earlier work by the research group in Halifax had shown that greater frailty severity was associated with not completing the CR program (e.g. among those with a FI of 0.2 or less 79.4% completed the program vs. 49.5% who had a frailty index 0.5 or more).8,9 This is an important observation as attending most sessions and completing the program are important determinants in achieving potential benefits from CR. Among those finishing the program, frailty severity as measured by the FI improved, especially among patients in the frailest category (FI improvements of 0.03 or greater were seen in 48% of those with a FI of < 0.2 on admission compared to 79% of those with an entry FI of > 0.05).8,9 Specific items making up the frailty index were found to be either positively or negatively related to the likelihood of completing the CR program.9 Some such as self-reported quality of life may not be considered as important determinants of CR program completion on entry but turned out to be. For those with perceived negative changes in their health, psychosocial interventions and support might lead to improved CR program attendance. Has the substantial work done by the Halifax-based research group on the relationship between frailty and CR7–9 changed the playing field and in what way? Collectively it does provide additional support for assessing frailty in cardiovascular practice and shows the potential benefits of CR programs in moderating frailty severity. In addition, the most recent work of the investigators shows that frailty status on entry to a CR program and changes in it during attendance are associated with 5-year outcomes that are both clinically and administratively important.7 The hazard, incidence rate, and odds ratios reported, though, were small (for each 0.01 change in the FI, there would be an approximately 1–3% corresponding change in the outcome examined) and not of a magnitude that would permit the use of the FI as a means to select who should or shouldn’t be offered CR. As noted by the authors,7 the determination of frailty severity and its contributors are likely of greatest benefit in helping to individualize the CR program in a manner that enhances both patient retention and frailty improvements arising from participation. While confirmatory studies are clearly needed as well as demonstration that targeted programming for CR patients with greater degrees of frailty do in fact lead to improved outcomes, these important contributions to our understanding of relationship between frailty and CR7–9 should be acknowledged. David Hogan

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,014
score de la tête « metaresearch » (Gemma)0,085
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,072

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,085
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,003
Communication savante0,0040,005
Science ouverte0,0010,002
Intégrité de la recherche0,0230,022
Charge utile insuffisante (le modèle a refusé de juger)0,0030,002

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 Opus0,044
Tête enseignante GPT0,333
Écart entre enseignants0,289 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentnon

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