The initial learning curve for the ROSA® Knee System can be achieved in 6-11 cases for operative time and has similar 90-day complication rates with improved implant alignment compared to manual instrumentation in total knee arthroplasty

Abstract

Purpose The purpose of this study was to determine the learning curve for total operative time using a novel cutting guide positioning robotic assistant for total knee arthroplasty (raTKA). Additionally, we compared complications and final limb alignment between raTKA and manual TKA (mTKA), as well as accuracy to plan for raTKA cases. Methods We performed a retrospective cohort study on a series of patients (n = 180) that underwent raTKA (n = 90) using the ROSA Total Knee System or mTKA (n = 90) by one of three high-volume (> 200 cases per year) orthopaedic surgeons between December 2019 and September 2020, with minimum three-month follow-up. To evaluate the learning curve surgical times and postoperative complications were reviewed. Results The cumulative summation analysis for total operative time revealed a change point of 10, 6, and 11 cases for each of three surgeons, suggesting a rapid learning curve. There was a significant difference in total operative times between the learning raTKA and both the mastered raTKA and mTKA groups (p = 0.001) for all three surgeons combined. Postoperative complications were minimal in all groups. The proportion of outliers for the final hip-knee-ankle angle compared to planned was 5.2% (3/58) for the mastered raTKA compared to 24.1% (19/79) for mTKA (p = 0.003). The absolute mean difference between the validated and planned resections for all angles evaluated was < 1 degree for the mastered raTKA cases. Conclusion As the digital age of medicine continues to develop, advanced technologies may disrupt the industry, but should not disrupt the care provided. This cutting guide positioning robotic system can be integrated relatively quickly with a rapid initial learning curve (6-11 cases) for operative times, similar 90-day complication rates, and improved component positioning compared to mTKA. Proficiency of the system requires additional analysis, but it can be expected to improve over time.