Domains of Frailty Predict Loss of Independence in Older Adults After Noncardiac Surgery
Notice bibliographique
Résumé
IMPORTANCE: Preoperative frailty has been consistently associated with death, severe complications, and loss of independence (LOI) after surgery. LOI is an important patient-centered outcome, but it is unclear which domains of frailty are most strongly associated with LOI. Such information would be important to target individual geriatric domains for optimization. OBJECTIVE: To assess whether impairment in individual domains of the Edmonton Frail Scale (EFS) can predict LOI in older adults after noncardiac surgery. DESIGN: Retrospective Cohort Study. SETTING: One Academic Hospital. PARTICIPANTS: Patients aged 65 or older who were living independently and evaluated with the EFS during a preoperative visit to the Center for Preoperative Optimization at the Johns Hopkins Hospital between June 2018 and January 2020. MAIN OUTCOME: LOI defined as discharge to increased level of care outside of the home with new mobility deficit or functional dependence. New mobility deficit and functional dependence were extracted from chart review of the standardized occupational therapy and physical therapy assessment performed before discharge. RESULTS: A total of 3497 patients were analyzed. Age (mean±SD) was 73.4±6.2 years, and 1579 (45.2%) were female. The median total EFS score was 3 (range 0-16), and 725/3497 (27%) were considered frail (EFS≥6). The frequencies of impairment in each EFS domain were functional performance (33.5% moderately impaired, 11% severely impaired), history of hospital readmission (42%), poor self-described health status (37%), and abnormal cognition (17.1% moderately impaired, 13.8% severely impaired). Overall, 235/3497 (6.7%) patients experienced LOI. Total EFS score was associated with LOI (odds ratio: 1.37, 95% CI, 1.30-1.45, P <0.001) in a model adjusted for age, sex, body mass index, American Society of Anesthesiologists rating, congestive heart failure, valvular heart disease, hypertension diagnosis, chronic lung disease, diabetes, renal failure, liver disease, weight loss, anemia, and depression. Using a nested log likelihood approach, the domains of functional performance, functional dependence, social support, health status, and urinary incontinence improved the base multivariable model. In cross-validation, total EFS improved the prediction of LOI with the final model achieving an area under the curve of 0.840. Functional performance was the single domain that most improved outcome prediction, but together with functional dependence, social support, and urinary incontinence, the model resulted in an area under the curve of 0.838. CONCLUSION AND RELEVANCE: Among domains measured by the EFS before a wide range of noncardiac surgeries in older adults, functional performance, functional dependence, social support, and urinary incontinence were independently associated with and improved the prediction of LOI. Clinical initiatives to mitigate LOI may consider screening with the EFS and targeting abnormalities within these domains.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».