Robotic-arm assisted unicompartmental knee arthroplasty system has a learning curve of 11 cases and increased operating time

Robotic-assisted unicompartmental knee arthroplasty: historical perspectives and current innovations

Robotic assisted unicompartmental knee arthroplasty (RAUKA) has emerged as a successful approach for optimizing implant positioning accuracy, minimizing soft tissue injury, and improving patient-reported outcomes. The application of RAUKA is expected to increase because of its advantages over conventional unicompartmental knee arthroplasty. This review article provides an overview of RAUKA, encompassing the historical development of the procedure, the features of the robotic arm and navigation systems, and the characteristics of contemporary RAUKA. The article also includes a comparison between conventional unicompartmental arthroplasty and RAUKA, as well as a discussion of current challenges and future advancements in the field of RAUKA.

The landscape of surgical robotics in orthopedics surgery

Orthopedic surgery is one of the first surgical specialties to apply surgical robotics in clinical practice, which has become an interesting field over the years with promising results. Surgical robotics can facilitate total joint arthroplasty by providing robotic support to accurately prepare the bone, improving the ability to reproduce alignment, and restoring normal kinematics. Various robotic systems are available on the market, each tailored to specific types of surgeries and characterized by a series of features with different requirements and/or modus operandi. Here, a narrative review of the current state of surgical robotic systems for total joint knee arthroplasty is presented, covering the different categories of robots, which are classified based on the operation, requirements, and level of interaction with the surgeon. The different robotic systems include closed/open platform, image-based/imageless, and passive/active/semi-active systems. The main goal of a robotic system is to increase the accuracy and precision of the operation regardless of the type of system. Despite the short history of surgical robots, they have shown clinical effectiveness compared to conventional techniques in orthopedic surgery. When considering which robotic system to use, surgeons should carefully evaluate the different benefits and drawbacks to select the surgical robot that fits their needs the best.

Learning curve for robot-assisted knee arthroplasty; optimizing the learning curve to improve efficiency

The introduction of robot-assisted (RA) systems in knee arthroplasty has challenged surgeons to adopt the new technology in their customized surgical techniques, learn system controls, and adjust to automated processes. Despite the potential advantages of RA knee arthroplasty, some surgeons remain hesitant to adopt this novel technology owing to concerns regarding the cumbersome adaptation process. This narrative review addresses the learning-curve issues in RA knee arthroplasty based on the existing literature. Learning curves exist in terms of the operative time and stress level of the surgical team but not in the final implant positions. The factors that reduce the learning curve are previous experience with computer-assisted surgery (including robot or navigation systems), specialization in knee surgery, high volume of knee arthroplasty, optimization of the RA workflow, sequential implementation of RA surgery, and consistency of the surgical team. Worse clinical outcomes may occur in the early postoperative period, but not in the later period, in RA knee arthroplasty performed during the learning phase. No significant differences were observed in implant survival or complication rates between the RA knee arthroplasties performed during the learning and proficiency phases.

The learning curve in robotic assisted knee arthroplasty is flattened by the presence of a surgeon experienced with robotic assisted surgery

PURPOSE

The purpose of this study was to investigate the learning curve associated with robotic assisted knee arthroplasty (RAS KA). Therefore, the evaluation of the influence of an experienced surgeon on the overall team performance of three surgeons regarding the learning curve in RAS KA was investigated. It was hypothesized that the presence of an experienced surgeon flattens the learning curve and that there was no inflection point for the learning curve of the surgical team.

METHODS

Fifty-five cases consisting of 31 total knee arthroplasties (TKA) and 24 unicompartmental arthroplasties (UKA) performed by three surgeons during 2021 were prospectively investigated. Single surgeon and team performance for operation time learning curve and inflection points were investigated using cumulative sum analysis (CUSUM).

RESULTS

A downward trend line for individual surgeons and the team performance regarding the operation time learning curve was observed. No inflexion point was observed for the overall team performance regarding TKA and UKA. The surgeon that performed all cases with the assistance of the experienced surgeon had significantly shorter surgical times than the surgeon that only occasionally received assistance from the experienced surgeon (p = 0.004 TKA; p = 0.002 UKA).

CONCLUSION

The presence of an experienced surgeon in robotically assisted knee arthroplasty can flatten the learning curve of the surgical team formerly unexperienced in robotic assisted systems. Manufacturers should provide expanded support during initial cases in centres without previous experience to robotic assisted knee arthroplasty.

LEVEL OF EVIDENCE

III.

Introduction of ROSA robotic-arm system for total knee arthroplasty is associated with a minimal learning curve for operative time

Purpose

The introduction of robotics for total knee arthroplasty (TKA) into the operating theatre is often associated with a learning curve and is potentially associated with additional complications. The purpose of this study was to determine the learning curve of robotic-assisted (RA) TKA within a multi-surgeon team.

Methods

This prospective cohort study included 83 consecutive conventional jig-based TKAs compared with 53 RA TKAs using the Robotic Surgical Assistant (ROSA) system (Zimmer Biomet, Warsaw, Indiana, USA) for knee osteoarthritis performed by three high-volume (> 100 TKA per year) orthopaedic surgeons. Baseline characteristics including age, BMI, sex and pre-operative Kellgren-Lawrence graded and Hip-Knee-Ankle Axis were well-matched between the conventional and RA TKA groups. Cumulative summation (CUSUM) analysis was used to assess learning curves for operative times for each surgeon. Peri-operative and delayed complications (infection, periprosthetic fracture, thromboembolism, and compromised wound healing) and revisions were reviewed.

Results

The CUSUM analysis for operative time demonstrated an inflexion point after 5, 6 and 15 cases for each of the three surgeons, or 8.7 cases on average. There were no significant differences (p = 0.53) in operative times between the RA TKA learning (before inflexion point) and proficiency (after inflexion point) phases. Similarly, the operative times of the RA TKA group did not differ significantly (p = 0.92) from the conventional TKA group. There was no discernible learning curve for the accuracy of component planning using the RA TKA system. The average length of post-operative follow-up was 21.3 ± 9.0 months. There was one revision for instability in the conventional TKA group and none in the RA TKA group. There were no significant difference (p > 0.99) in post-operative complication rates between the conventional TKA and RA TKA groups.

Conclusions

The introduction of the RA TKA system was associated with a learning curve for operative time of 8.7 cases. Operative times between the RA TKA and conventional TKA group were similar. The short learning curve implies this RA TKA system can be adopted relatively quickly into a surgical team with minimal risks to patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40634-022-00524-5.

Robotic-arm assisted total knee arthroplasty has a learning curve of 16 cases and increased operative time of 12 min

BACKGROUND

Robotic-arm assisted systems are increasingly used for knee arthroplasty, however introduction of new systems can involve a learning curve. We aimed to define the learning curve in terms of operative time and component placement/sizing of a robotic system for total knee arthroplasty (TKA) in a team of experienced surgeons, and to investigate mid-term patient outcomes.

METHODS

A total of 101 consecutive patients underwent primary robotic-arm assisted TKA by three surgeons (mean 2 year follow-up). Operative times, component placement, implant sizing and reoperations were recorded. Cumulative Summation (CUSUM) was used to analyse learning curves. Patient outcomes were compared between learning and proficiency phases.

RESULTS

The learning curve was 16 cases, with a 12-min increase in operative time (P < 0.01). Once proficiency was achieved, the greatest time reductions were seen for navigation registration (P = 0.003) and bone preparation (P < 0.0001). A learning curve was found with polyethylene (PE) insert sizing (P = 0.01). No differences were found between learning and proficiency groups in terms of implant survival (100% and 97%, respectively, NS) or patient-reported outcome measures at 2 years (NS).

CONCLUSION

Introduction of a robotic-arm assisted system for TKA led to increased operative times for navigation registration and bone preparation, and a learning curve with PE insert sizing. No difference in patient outcomes between learning and proficiency groups at 2 years was found. These findings can inform surgeons' expectations when starting to use robotic-assisted systems.

Robotic-Assisted Versus Manual Unicompartmental Knee Arthroplasty: A Time-Driven Activity-Based Cost Analysis

BACKGROUND

The cost-effectiveness of robotic-assisted unicompartmental knee arthroplasty (RA-UKA) remains unclear. Time-driven activity-based costing (TDABC) has been shown to accurately reflect true resource utilization. This study aimed to compare true facility costs between RA-UKA and conventional UKA.

METHODS

We identified 265 consecutive UKAs (133 RA, 132 conventional) performed at a specialty hospital in 2016-2020. Itemized facility costs were calculated using TDABC. Separate analyses including and excluding implant costs were performed. Multiple regression was performed to determine the independent effect of robotic assistance on facility costs.

RESULTS

Due to longer operative time, RA-UKA patients had higher personnel costs and total facility costs ($2,270 vs $1,854, P < .001). Controlling for demographics and comorbidities, robotic assistance was associated with an increase in personnel costs of $399.25 (95% confidence interval [CI] $343.75-$454.74, P < .001), reduction in supply costs of $55.03 (95% CI $0.56-$109.50, P = .048), and increase in total facility costs of $344.27 (95% CI $265.24-$423.31, P < .001) per case. However, after factoring in implant costs, robotic assistance was associated with a reduction in total facility costs of $235.87 (95% CI $40.88-$430.85, P < .001) per case.

CONCLUSION

Using TDABC, overall facility costs were lower in RA-UKA despite a longer operative time. To facilitate wider adoption of this technology, implant manufacturers may negotiate lower implant costs based on volume commitments when robotic assistance is used. These supply cost savings appear to offset a portion of the increased costs. Nonetheless, further research is needed to determine if RA-UKA can improve clinical outcomes and create value in arthroplasty.