Background: Within the scope of the EU project EUPHORIC a methodology for direct comparison of different datasets was developed and applied on a sample of implants, among them the Oxford Unicompartmental Knee Arthroplasty (Oxford Uni). The aim was to identify potential bias factors inherent in the datasets and evaluate the outcome achieved with this implant.
Materials and Methods: A structured comparison was performed of data published on the revision rate of the Oxford Unicompartmental prosthesis. Both clinical follow-up studies published in Medline-listed journals and worldwide Register data were included. The data were stratified with regard to potential influence factors like the individual research groups or the geographical origin of the papers.
Results: A major proportion of the published data, between 50 and 75%, depending on the method of calculation, comes from studies including the developing institution in Oxford. The results published by this group deviates statistically significantly from the reference datasets from Register data or independent research groups. Data from the developing hospital show mean revision rates that are 4.4 times lower than those based on worldwide Register data, and 2.74 times lower than in independent studies. As opposed to this, independent studies on average publish data that are reproducible in Register data.
Conclusion: A conventional meta-analysis of clinical studies is significantly affected through the influence of the developing institution and is therefore subject to a bias. Neither through arthroplasty Register outcome data nor by other research groups that have disclosed outcome information on the Oxford Uni can the excellent results be reproduced that were published by the inventors.
Compared to other implants for unicompartmental knee arthroplasty in worldwide arthroplasty Registers, the Oxford Uni shows good results.
For the assessment of the outcome of implants, register data are to be rated superior and, in terms of reference data for the detection of potential bias factors in the clinical literature, can provide an essential contribution for scientific meta-analyses.