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Can a Matched Case-Control Methodology Efficiently Estimate Functional Relationships Between Knee Implant Alignment
Matthew D Hickey1, Carolyn Anglin2, Bassam Masri3
1School of Biomedical Engineering, The University of British Columbia, Vancouver, British Columbia, Canada.
Background:
Conventional randomized controlled trials are generally too underpowered to yield meaningful insights into the functional dependence of revision risk on surgeon-controlled implant alignment. However, matched case-control studies focused on patients undergoing revision surgery could produce such insights. We therefore asked: can we determine, through simulation, whether such matched case-control study designs could potentially produce sufficiently accurate estimates of the functional relationships between surgeon-controlled variables and aseptic revision risk to inform surgical alignment targets for total knee arthroplasty?
Methods:
We evaluated the potential for a matched case-control methodology to achieve this goal using a simulation approach in which we characterized individual patients' risk of revision by implant life factor (ILF) functions that reflected the effects of both surgeon-controlled and patient-specific factors. We then synthesized simulated patients, emulated the matching process, and trained Naïve Bayes classifiers to estimate the influence of surgeon-controlled factors on implant survival. We repeated this process for various potential clinical study sizes and then calculated the errors in both the estimated ILF functions associated with the surgeon-controlled factors and the predicted optimal implant alignment.
Results:
Across different study sizes, our classifier predicted the simulated functional relationships between ILF variables and optimal implant placement with reasonable accuracy. With as few as 300 revision candidates, we estimated the weighted absolute mean errors in predicting the ILF to be 3.3 ± 0.9% for coronal alignment, 2.6 ± 1.0% for tibial slope, and 5.4 ± 0.8% for femoral component rotation (relative to the transepicondylar axis). We predicted the optimal implant orientation to within 1.5 ± 1.2° for coronal alignment, 0.2 ± 1.2° for tibial slope, and 0 ± 0° for femoral component rotation.
Conclusions:
Based on these simulations, it seems that a matched case-control methodology may represent an acceptably efficient approach to determining the impact of surgeon-controlled variables on the risk of aseptic revision in total knee arthroplasty.
