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Enregistrement W2618140211

COST-EFFECTIVENESS OF PATELLAR RESURFACING COMPARED WITH NON-RESURFACING IN PRIMARY TOTAL KNEE ARTHROPLASTY

2017· article· en· W2618140211 sur OpenAlexaffabout
Edward M. Vasarhelyi, Colleen A. Weeks, Stephen E. Graves, Lauren E. Kelly

Notice bibliographique

RevueJournal of Bone and Joint Surgery-british Volume · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueTotal Knee Arthroplasty Outcomes
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésMedicineOxford knee scorePatellaTotal knee arthroplastyAnterior knee painSurgeryCohortArthroplastyDemographicsPhysical therapyEvidence-based medicineQuality of life (healthcare)OsteoarthritisInternal medicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background The management of the patella during primary total knee arthroplasty (TKA) is controversial. Despite the majority of patients reporting excellent outcomes following TKA, a common complaint is anterior knee pain. Resurfacing of the patella at the time of initial surgery has been proposed as a means of preventing anterior knee pain, however current evidence, including four recent meta-analyses, has failed to show clear superiority of patellar resurfacing. Therefore, the purpose of this study was to estimate the cost-effectiveness of patellar resurfacing compared to non-resurfacing in TKA. Methods We conducted a cost-effectiveness analysis using a decision analytic model to represent a hypothetical patient cohort undergoing primary TKA. Each patient will receive a TKA either with the Patella Resurfaced or Not Resurfaced. Following surgery, patients can transition to one of three chronic health states: 1) Well Post-operative, 2) Patellofemoral Pain (PFP), or 3) Serious Adverse Event (AE), which we have defined as any event requiring Revision TKA, including: loosening/lysis, infection, instability, or fracture (Figure 1). We obtained revision rates following TKA for both resurfaced and unresurfaced cohorts using data from the 2014 Australian Registry. This data was chosen due to similarities between Australian and North American practice patterns and patient demographics, as well as the availability of longer term follow up data, up to 14 years postoperative. Our effectiveness outcome for the model was the quality-adjusted life year (QALY). We used utility scores obtained from the literature to calculate QALYs for each health state. Direct procedure costs were obtained from our institution9s case costing department, and the billing fees for each procedure. We estimated cost-effectiveness from a Canadian publicly funded health care system perspective. All costs and quality of life outcomes were discounted at a rate of 5%. All costs are presented in 2015 Canadian dollars. Results Our cost-effectiveness analysis suggests that TKA with patella resurfacing is a dominant procedure. Patients who receive primary TKA with non-resurfaced patella had higher associated costs over the first 14 years postoperative ($16,182 vs $15,720), and slightly lower quality of life (5.37 QALYs vs 6.01 QALYs). The revision rate for patellar resurfacing was 1.3%. If the rate of secondary resurfacing procedures is 0.5% or less, there is no difference in costs between the two procedures. Discussion Our results suggest that, up to 14 years postoperative, resurfacing the patella in primary TKA is cost-effective compared to primary TKA without patellar resurfacing, due to the higher revision rate in this cohort of patients for secondary resurfacing. Our sensitivity analysis suggests that, among surgical practices that do not routinely perform secondary resurfacing procedures (estimated rate at our institution is 0.3%) there is no significant difference in costs. Although our results suggest that patella resurfacing results in higher quality of life, our model is limited by the availability and validity of utility outcome estimates reported in the literature for the long term follow up of patients following TKA with or without patella resurfacing and secondary resurfacing procedures.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,030
Tête enseignante GPT0,257
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2017
Routes d'admission2
Résumé présentoui

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