Is Frailty Associated with Adverse Outcomes After Orthopaedic Surgery?
Notice bibliographique
Résumé
Background: There is increasing evidence supporting the association between frailty and adverse outcomes after surgery. There is, however, no consensus on how frailty should be assessed and used to inform treatment. In this review, we aimed to synthesize the current literature on the use of frailty as a predictor of adverse outcomes following orthopaedic surgery by (1) identifying the frailty instruments used and (2) evaluating the strength of the association between frailty and adverse outcomes after orthopaedic surgery. Methods: A systematic review was performed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Scopus, and the Cochrane Central Register of Controlled Trials were searched to identify articles that reported on outcomes after orthopaedic surgery within frail populations. Only studies that defined frail patients using a frailty instrument were included. The methodological quality of studies was assessed using the Newcastle-Ottawa Scale (NOS). Study demographic information, frailty instrument information (e.g., number of items, domains included), and clinical outcome measures (including mortality, readmissions, and length of stay) were collected and reported. Results: The initial search yielded 630 articles. Of these, 177 articles underwent full-text review; 82 articles were ultimately included and analyzed. The modified frailty index (mFI) was the most commonly used frailty instrument (38% of the studies used the mFI-11 [11-item mFI], and 24% of the studies used the mFI-5 [5-item mFI]), although a large variety of instruments were used (24 different instruments identified). Total joint arthroplasty (22%), hip fracture management (17%), and adult spinal deformity management (15%) were the most frequently studied procedures. Complications (71%) and mortality (51%) were the most frequently reported outcomes; 17% of studies reported on a functional outcome. Conclusions: There is no consensus on the best approach to defining frailty among orthopaedic surgery patients, although instruments based on the accumulation-of-deficits model (such as the mFI) were the most common. Frailty was highly associated with adverse outcomes, but the majority of the studies were retrospective and did not identify frailty prospectively in a prediction model. Although many outcomes were described (complications and mortality being the most common), there was a considerable amount of heterogeneity in measurement strategy and subsequent strength of association. Future investigations evaluating the association between frailty and orthopaedic surgical outcomes should focus on prospective study designs, long-term outcomes, and assessments of patient-reported outcomes and/or functional recovery scores. Clinical Relevance: Preoperatively identifying high-risk orthopaedic surgery patients through frailty instruments has the potential to improve patient outcomes. Frailty screenings can create opportunities for targeted intervention efforts and guide patient-provider decision-making.
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 | 0,012 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».