Nicholas M Brisson1, Anthony A Gatti2, Philipp Damm3, Georg N Duda3, Monica R Maly4. 1. Charité-Universitätsmedizin Berlin, Berlin, Germany, and McMaster University, Hamilton, Ontario, Canada. 2. McMaster University and NeuralSeg, Hamilton, Ontario, Canada. 3. Charité-Universitätsmedizin Berlin, Berlin, Germany. 4. McMaster University, Hamilton, Ontario, Canada, and University of Waterloo, Waterloo, Ontario, Canada.
Abstract
OBJECTIVE: The relationship between in vivo knee load predictions and longitudinal cartilage changes has not been investigated. We undertook this study to develop an equation to predict the medial tibiofemoral contact force (MCF) peak during walking in persons with instrumented knee implants, and to apply this equation to determine the relationship between the predicted MCF peak and cartilage loss in patients with knee osteoarthritis (OA). METHODS: In adults with knee OA (39 women, 8 men; mean ± SD age 61.1 ± 6.8 years), baseline biomechanical gait analyses were performed, and annualized change in medial tibial cartilage volume (mm3 /year) over 2.5 years was determined using magnetic resonance imaging. In a separate sample of patients with force-measuring tibial prostheses (3 women, 6 men; mean ± SD age 70.3 ± 5.2 years), gait data plus in vivo knee loads were used to develop an equation to predict the MCF peak using machine learning. This equation was then applied to the knee OA group, and the relationship between the predicted MCF peak and annualized cartilage volume change was determined. RESULTS: The MCF peak was best predicted using gait speed, the knee adduction moment peak, and the vertical knee reaction force peak (root mean square error 132.88N; R2 = 0.81, P < 0.001). In participants with knee OA, the predicted MCF peak was related to cartilage volume change (R2 = 0.35, β = -0.119, P < 0.001). CONCLUSION: Machine learning was used to develop a novel equation for predicting the MCF peak from external biomechanical parameters. The predicted MCF peak was positively related to medial tibial cartilage volume loss in patients with knee OA.
OBJECTIVE: The relationship between in vivo knee load predictions and longitudinal cartilage changes has not been investigated. We undertook this study to develop an equation to predict the medial tibiofemoral contact force (MCF) peak during walking in persons with instrumented knee implants, and to apply this equation to determine the relationship between the predicted MCF peak and cartilage loss in patients with knee osteoarthritis (OA). METHODS: In adults with knee OA (39 women, 8 men; mean ± SD age 61.1 ± 6.8 years), baseline biomechanical gait analyses were performed, and annualized change in medial tibial cartilage volume (mm3 /year) over 2.5 years was determined using magnetic resonance imaging. In a separate sample of patients with force-measuring tibial prostheses (3 women, 6 men; mean ± SD age 70.3 ± 5.2 years), gait data plus in vivo knee loads were used to develop an equation to predict the MCF peak using machine learning. This equation was then applied to the knee OA group, and the relationship between the predicted MCF peak and annualized cartilage volume change was determined. RESULTS: The MCF peak was best predicted using gait speed, the knee adduction moment peak, and the vertical knee reaction force peak (root mean square error 132.88N; R2 = 0.81, P < 0.001). In participants with knee OA, the predicted MCF peak was related to cartilage volume change (R2 = 0.35, β = -0.119, P < 0.001). CONCLUSION: Machine learning was used to develop a novel equation for predicting the MCF peak from external biomechanical parameters. The predicted MCF peak was positively related to medial tibial cartilage volume loss in patients with knee OA.