Use of Screening Tools for Frailty and Sarcopenia amongst Older Persons in Medical Outpatient Clinics to Facilitate Care Integration
Notice bibliographique
Résumé
Objectives: The importance of screening for frailty and sarcopenia has grown. They have both been shown to be associated with disability, mortality and poor healthcare outcomes. With increasing subspecialisation in tertiary healthcare institutions, at risk older adults often receive fragmented care from organ-specific subspecialists. Several tools to do so exist and this study aimed to examine if the SARC-F and Edmonton frail screening tools are useful in clinical practice to identify at risk patients for intervention.Methods: This is a cross-sectional study of patients attending medical specialist outpatient clinics at the National University Hospital, Singapore from May 2015 to August 2016. Frailty and sarcopenia were identified using the Edmonton Frail Scale and SARC-F questionnaires respectively. Other clinically relevant data including basic demographics, presence of caregiver, number of follow-ups, medications and hospital readmissions in the past 1 year, Charlson’s comorbidity index and their Modified Barthel’s Index were collected.Results: A total of 115 patients 65years old and above were screened. The mean age of all patients was 76.6±6.5 years. 52.2% were female and 75.7% (n=87) were of Chinese ethnicity. 50% (n=57) of patients were independent and did not require a caregiver. Of the sample,44% (n=51) of patients were sarcopenic while 27% (n=31) were classified as frail. 23% (n=27) were both frail and sarcopenic. Women were more likely to be frail (67.7% vs 32.3%, p=0.042) and sarcopenic (58.3% vs 29.0%, p=0.001).Sarcopenic patients had a higher Charlson Comorbidities Index (5.0 vs 6.6, p=0.001) and lower modified Barthel’s Index (33 vs 78, p=0.001). Being sarcopenic was associated with a higher likelihood of having a caregiver (p=0.001) with an increasing dependence on children and domestic helpers. They had an average of 3.0 medical specialty follow ups compared to 2.3 follow ups for non-sarcopenic patients (p=0.004). Sarcopenia was significantly associated with polypharmacy (74.5% vs 42.1%, p=0.001), more than 2 hospital readmissions within a year (23.5% vs 9.4%, p=0.043), a higher number of falls (1.20 vs 0.17, p<0.001) and falls with significant consequences (0.14 vs. 0.02, p<0.001).Frail patients similarly had a higher Charlson Comorbidities Index (6.7 vs 5.3, p=0.013) and lower Modified Barthel’s Index (78 vs 97, p<0.001). They had 2.9 vs 2.1 specialty follow ups (p = 0.032). Frailty is associated with polypharmacy (87.1% vs. 45.2%, p<0.001), more than 2 hospital readmissions yearly (66.7% vs 33.3%, p<0.001), a higher number of falls (1.39 vs 0.35, p=0.001) and falls with significant consequences (0.16 vs 0.04, p=0.019).Conclusions: The prevalence of frailty and sarcopenia among elderly patients is high. Both syndromes are predictors of recurrent hospital admissions, polypharmacy, multiple medical clinic appointments, higher rate of falls and falls with serious consequences. Using simple screening tools to identify such at risk elderly to facilitate streamlining of care and care integration is likely to be beneficial and cost effective in the long run.
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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».