Frailty in the over 65’s undergoing elective surgery (FIT-65) – a three-day study examining the prevalence of frailty in patients presenting for elective surgery
Notice bibliographique
Résumé
BACKGROUND: Frailty increases the risk of perioperative complications, length of stay, and the need for assisted-living after discharge. As the UK population ages the number of frail patients presenting for elective surgery in the UK is likely to grow. Despite the potential benefits of early diagnosis, frailty is not uniformly screened for in UK elective surgical patients and its prevalence remains unclear. The primary aim of this study was to assess the prevalence of frailty in patients aged over 65 years undergoing elective surgery. METHODS: We performed a prospective cross-sectional observational study in eight UK hospitals. Data were collected over three consecutive days with follow-up at 30 days. HRA approval was obtained (REC 20/SC/0121) and signed informed consent obtained. Participants were eligible for inclusion if they were 65 years or older and undergoing elective surgery. Pre-operative data were collected from hospital notes by anaesthetic trainees. A member of the research team blinded to the pre-operative dataset screened each participant for frailty pre-operatively using the Reported Edmonton Frail Scale (REFS). Post-operative data were collected from the notes on day of surgery and at 30 days. Participants were defined as "frail" if they scored 8 or more on the REFS. RESULTS: Two hundred twenty eight participants were recruited during the study period of whom 218 proceeded to surgery. There were 103 females and 115 males. Median age was 75 years (interquartile range 70-80). Thirty-seven participants (17.0%) were identified as frail. Frail patients were older, had a higher ASA score, were more likely to have carers and were more likely to be anaemic or present with ECG abnormalities. There were no differences in gender, BMI, place of residence or smoking status for patients identified as frail versus non-frail. There was no difference in length-of-stay between frail and non-frail patients, although those identified as frail were less likely to be discharged to their own home. CONCLUSION: We found the prevalence of frailty in a mixed population of elective surgical patients aged 65 or over to be 17.0%. Furthermore, we found the REFS to be a practical tool for pre-operative frailty screening. Frail patients presented for elective surgery with modifiable co-morbidities which could have been optimised pre-operatively. Early screening could highlight frail patients, allowing time for pre-operative planning and evidence-based optimisations of comorbidities. We therefore encourage the adoption of frailty assessment as a routine part of pre-operative assessment.
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 | 0,002 |
| 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 | 0,001 | 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) | 0,001 | 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 ».