Provider volume and other predictors of outcome after total knee arthroplasty: a population study in Ontario.
Notice bibliographique
Résumé
INTRODUCTION: Because of rationing of the limited pool of health care resources, access to total knee arthroplasty (TKA) is limited, but investigation of variables that predict complications, length of hospital stay, cost and outcomes of TKA may allow us to optimize the available resources. The objective of this study was to examine the effect of various factors on complication rates after TKA in patients managed in Ontario. METHODS: Patients who had undergone an elective TKA between 1993 and 1996, as captured in the Canadian Institute for Health Information (CIHI) database, formed the study cohort. The CIHI dataset was used to obtain information regarding in-hospital complications, hospital length of stay, revision rates, infection rates and mortality. Generalized estimating linear or logistic regression equations were used to model outcomes as a function of age, gender, comorbidity, diagnosis and provider volume. RESULTS: During the study period, 14,352 patients in Ontario underwent TKA. Mortality at 3 months was associated with patient age, gender and comorbidity. There was no association between provider volume and mortality or the infection rate. Higher revision rates at 1 and 3 years were significantly associated with lower patient age and low hospital volume (p < 0.05). Hospitals in which fewer than 48 TKA procedures were done per year (< 40th percentile) had 2.2-fold greater 1-year revision rates than hospitals performing more than 113 TKAs annually (> 80th percentile). Complications during admission were associated with increased patient age and comorbidity, and higher hospital volume. Longer hospital stay was associated with female gender, increasing patient comorbidity and age, and lower provider volume. Surgeons who performed fewer than 14 TKAs annually (< 40th percentile) kept patients in hospital an average of 1.4 days longer than surgeons performing more than 42 TKAs annually (> 80th percentile). CONCLUSIONS: Patient variables significantly affect the rate of complications. Age, sex and comorbidity were significant predictors of complications, length of hospital stay and mortality after TKA. Although low surgeon volume was related to longer hospital stay, there was no association between surgeon volume and complication rates. The increased early revision rate for low-volume hospitals demands further study.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».