ABSTRACT
BACKGROUND: Osteophytes are commonly used to diagnose and guide knee osteoarthritis (OA) treatment, but their causes are unclear. Although they are not typically the focus of knee arthroplasty surgeons, they can predict case difficulty and length. Furthermore, their extent and location may yield much information about the knee joint status. The aims of this computed tomography-based study in patients awaiting total or partial knee arthroplasty were to: (1) measure osteophyte volume in anatomical subregions and relative change as total volume increases; (2) determine whether medial and/or lateral OA affects osteophyte distribution; and (3) explore relationships between osteophytes and OA severity. METHODS: Data were obtained from 4,928 computed tomography scans. Machine-learning-based imaging analyses enabled osteophyte segmentation and quantification, divided into anatomical regions. Mean three-dimensional joint space narrowing was assessed in medial and lateral compartments. A Bayesian model assessed the uniformity of osteophyte distribution. We correlated femoral osteophyte volumes with B-scores, a validated OA status measure. RESULTS: Total tibial (25%) and femoral osteophyte volumes (75%) within each knee correlated strongly (R2 = 0.85). Medial osteophytes (65.3%) were larger than lateral osteophytes (34.6%), with similar proportions in both the femur and tibia. Osteophyte growth was found in all compartments, and as total osteophyte volume increased, the relative distribution of osteophytes between compartments did not markedly change. No evidence of variation was found in the regional distribution of osteophyte volume between knees with medial, lateral, both, or no three-dimensional joint space narrowing in the femur or tibia. There was a direct relationship between osteophyte volume and OA severity. CONCLUSIONS: Osteophyte volume increased in both medial and lateral compartments proportionally with total osteophyte volume, regardless of OA location. The peripheral position of femoral osteophytes does not appear to contribute to load-bearing. This suggests that osteophytic growth represents a 'whole-knee'/global response. This work may have broad applications for knee OA, both surgically and nonoperatively.
ABSTRACT
Robotic-assisted total knee arthroplasty (RA-TKA) has been shown to improve the accuracy of bone resection, reduce radiographic outliers, and decrease iatrogenic injury. However, it has also been shown that RA-TKA surgical times can be longer than manual surgery during adoption. The purpose of this article was to investigate (1) the characteristics of the operative time curves and trends, noting the amount of surgeons who improved, for those who performed at least 12 cases (based on initial modeling); (2) the proportion of RA surgeons who achieved the same operative times for RA-TKA as compared with manual TKAs; and (3) the number of RA-TKA cases until a steady-state operative time was achieved. TKA operative times were collected from 30 hospitals for 146 surgeons between January 1, 2016, and December 31, 2019. A hierarchical Bayesian model was used to estimate the difference between the mean RA-TKA times by case interval and the weighted baseline for manual times. The learning curve was observed at the 12th case. Therefore, operative times were analyzed for each surgeon who performed at least 12 RA-TKA cases to determine the percentage of these surgeons who trended toward a decrease or increase in their times. These surgeons were further analyzed to determine the proportion who achieved the same operating times as manual TKAs. A further hierarchical Bayesian model was used to determine when these surgeons achieved steady-state operative times. There were 60 surgeons (82%) who had decreasing surgical times over the first 12 RA-TKA cases. The remaining 13 (18%) had increasing surgical times (mean increase of 0.59 minutes/case). Approximately two-thirds of the surgeons (64%) achieved the same operating times as manual cases. The steady-state time neutrality occurred between 15 and 20 cases and beyond. This study demonstrated the learning curve for a large cohort of RA-TKAs. This model demonstrated a learning curve between 15 and 20 cases and beyond. These are important findings for this innovative technology.
