Prospective Assessment of Surgical Recovery Using a Rapid Evaluation of Frailty in Elderly Prolapse Patients (REFINE)
Notice bibliographique
Résumé
INTRODUCTION: Patients undergoing pelvic organ prolapse (POP) surgery are often elderly (>/=65 years) and could benefit from a frailty assessment to better predict perioperative outcomes. Assessing frailty provides an additional guage of medical and perioperative risks than age alone. The Clinical Frailty Scale (CFS-9) is a validated 9-point rapid frailty assessment tool that can be easily incorporated into the urogynecologic setting to characterize patients prior to surgery. OBJECTIVE: This study compared functional status at baseline and at 6 weeks after POP surgery in non-frail vs. frail elderly patients. Secondary outcomes included a comparison of cognitive testing and adverse events between these populations. METHODS: This is a prospective, multi-institution cohort study of elderly patients undergoing surgery for POP. Patients >/=65 years were identified at their preoperative visit, and frailty was assessed using the CFS-9. Patients were excluded if they were undergoing combined surgery with another specialty or surgery for oncologic purposes. Baseline functional status was assessed using the Activities Assessment Scale (AAS, maximum score of 100), and cognitive testing was performed using the Montreal Cognitive Assessment, 5-minute protocol (mini-MoCA, maximum score of 15). Frailty was defined as CSF-9 score >/=4. The AAS and Mini-MoCA were then repeated at the 6–8 week postoperative visit and adverse events were recorded. Student's t-test was used to compare continuous variables and chi-squared test was applied to categorical variables. Generalized linear models were developed for continuous outcomes controlling for demographics, Charlson Comorbidity Index (CCI), degree of prolapse, urinary tract infection, and aggregate adverse events. Means and 95% confidence intervals (CIs) were reported. Statistical analyses were performed using R (v4.4.1). RESULTS: 185 patients were included, of which 39 were frail. Patients in the frail group were older (mean age 74 vs 72; P=0.004), had a higher mean BMI (28.6 vs 26.7; P=0.044), and had a higher mean CCI (4.2 vs 3.5; P=0.002) (Table 1). Frail patients were more likely to be non-White (26% vs 9%; P=0.011) and have stage 4 prolapse (26% vs 8%; P=0.004). Both preoperative (67.9 vs 90.5; P=<0.001) and postoperative (76.7 vs 91.9; P=<0.001) AAS scores were lower for the frail group (Table 2). A similar trend was seen for the mini-MoCA with lower scores both preoperatively (11.9 vs 12.7; P=0.020) and postoperatively (12.3 vs 13.3; P=0.001) in frail patients. Both groups had higher AAS scores after surgery, but the mean difference in the AAS was greater for the frail group (8.6 vs 1.6; P=0.023), indicating that there was greater improvement in functional status after prolapse surgery in patients who were frail. This remained significant in the multivariate model (95% CI 2–11.89, P=0.01). Neither group showed significant change in cognition (0.4 vs 0.6; P=0.85), and adverse events after surgery were not greater in frail patients (23% vs 13%; P=0.13). CONCLUSIONS: Assessing frailty prior to POP surgery is feasible with rapid assessment tools. Identifying frail patients with lower baseline functional status allows the opportunity for optimization of functional status prior to surgery. Future studies could aim to assess the value of a pre-habilitation program on frail patients' perioperative outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».