Satisfaction following primary total knee arthroplasty (TKA) remains stagnant at approximately 80%, leaving 1 in 5 patients unsatisfied with their procedure. In an attempt to shatter this ceiling, the orthopedic community has aggressively adopted advanced technologies, like robotic assistance, which provide surgeons with enhanced precision for implanting TKA components.
However, there is a glaring blind spot in this technological revolution: while surgical precision has shown improvement, data on the true accuracy of optimal implant alignment are lacking, which are necessary for these advanced technologies to become truly valuable. You can execute a bone cut perfectly with a robot, but if your programmed alignment targets are flawed, your patient outcomes will suffer. We deployed advanced machine learning to finally identify the specific multi-planar alignment zones that actually correlate with achieving minimal clinically important differences (MCIDs) in patient-reported outcome measures (PROMs).
