Learning curve for active robotic total knee arthroplasty.
Academic Article
Overview
abstract
PURPOSE: Total Knee Arthroplasty (TKA) procedures incorporate technology in an attempt to improve outcomes. The Active Robot (ARo) performs a TKA with automated resections of the tibia and femur in efforts to optimize bone cuts. Evaluating the Learning Curve (LC) is essential with a novel tool. The purpose of this study was to assess the associated LC of ARo for TKA. METHODS: A multi-center prospective FDA cohort study was conducted from 2017 to 2018 including 115 patients that underwent ARo. Surgical time of the ARo was defined as Operative time (OT), segmented as surgeon-dependent time (patient preparation and registration) and surgeon-independent time (autonomous bone resection by the ARo). An average LC for all surgeons was computed. Complication rates and patient-reported outcome (PRO) scores were recorded and examined to evaluate for any LC trends in these patient related factors. RESULTS: The OT for the cases 10-12 were significantly quicker than the OT time of cases 1-3 (p < 0.028), at 36.5 ± 7.4 down from 49.1 ± 17 min. CUSUM and confidence interval analysis of the surgeon-dependent time showed different LCs for each surgeon, ranging from 12 to 19 cases. There was no difference in device related complications or PRO scores over the study timeframe. CONCLUSION: Active Robotic total knee arthroplasty is associated with a short learning curve of 10-20 cases. The learning curve was associated with the surgical time dedicated to the robotic specific portion of the case. There was no learning curve-associated device-related complications, three-dimensional component position, or patient-reported outcome scores. LEVEL OF EVIDENCE: Level II.
