P4514Agreement and prognostic significance of 6 frailty tools in patients with chronic heart failure
Notice bibliographique
Résumé
Abstract Background Frailty is common in patients with chronic heart failure (CHF) and is associated with adverse outcome. Many frailty tools are available, however, there is no standard way of evaluating frailty in patients with CHF. Purpose To report the prevalence of frailty, agreement and prognostic significance amongst 3 frailty assessment tools and 3 screening tools in CHF patients. Methods We comprehensively studied frailty using 6 frailty tools. Frailty screening tools include: Clinical frailty scale (CFS); Derby frailty index & Acute frailty network frailty criteria. Frailty assessment tools include: Fried criteria; Edmonton frailty score & Deficit index. Since there is no gold standard in evaluating frailty in CHF patients, for each of the frailty tools, we used the results of the other 5 tools to produce a combined frailty index which we used as a “standard” frailty tool. Subjects were defined as frail if so identified by at least 3 out of 5 tools. Results 467 consecutive ambulatory CHF patients (67% male, median age 76 (IQR: 69–82) years, median NTproBNP 1156 (IQR: 469–2463) ng/L) and 87 controls (79% male, median age 73 (IQR: 69–77 years) were studied. Prevalence of frailty was much higher in CHF patients than in controls (30–52% vs 2–15%, respectively). Amongst the frailty screening tools, DFI scored the greatest proportion of patients as frail (48%) while CFS scored the lowest proportion as frail (44%). Amongst the assessment tools, Fried criteria scored the greatest proportion of patients as frail (52%) while EFS scored the lowest proportion as frail (30%). Frail patients were older, have worse symptoms, higher NTproBNP and more co-morbidities compared to non-frail patients. Of the screening tools, CFS had the strongest agreement with assessment tools (kappa coefficient: 0.65–0.72, all p<0.001). CFS had the highest sensitivity (87%) and specificity (89%) amongst screening tools and the lowest misclassification rate (12%) amongst all 6 frailty tools in identifying frailty according to the combined frailty index. During a median follow-up of 559 days (IQR 512–629 days), 82 (18%) patients died. 55% (N=45) of frail patients died of non-cardiovascular causes. Worsening frailty as detected by all 6 frailty tools was associated with worse outcome. A base model for mortality prediction including sex, NYHA class (III/IV vs I/II), BMI, log NTproBNP and haemoglobin had a C-statistics of 0.78. Amongst frailty tools: CFS and Fried criteria increased model performance most compared with base model (c-statistics: 0.80 for both). Patients who were frail according to CFS had a 9 times greater mortality risk than non-frail patients (Figure). Conclusion Frailty is common in CHF patients and is associated with worse outcome. CFS is a simple screening tool which identifies a similar group as lengthy assessment tools and has similar prognostic significance. Frailty screening should be incorporated into routine care of patients with CHF. Acknowledgement/Funding 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 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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| 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 | 0,001 | 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 ».