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Record W2618140211

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

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

Bibliographic record

VenueJournal of Bone and Joint Surgery-british Volume · 2017
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineOxford knee scorePatellaTotal knee arthroplastyAnterior knee painSurgeryCohortArthroplastyDemographicsPhysical therapyEvidence-based medicineQuality of life (healthcare)OsteoarthritisInternal medicine
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.257
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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