PREDICTING GAIT KINETIC OUTCOMES USING KINEMATICS IN PATIENTS BEFORE AND AFTER TOTAL KNEE ARTHROPLASTY

Kinetic outcomes during walking are important measures in understanding patient variability in total knee arthroplasty (TKA) outcomes [1,2] and implant stability [3]. However, capturing gait kinematic outcomes can be done more easily than kinetic outcomes using various motion capture systems. High fidelity kinetic gait outcomes typically require a synchronized system of photoelectric motion capture and in-ground force plates [1,2], therefore being constrained mostly to sophisticated laboratory settings. With a long-term goal of accessible kinetic outcome capture in clinical environments, this study examines the associations among gait kinetic outcomes relevant to TKA and gait kinematic measures in a cohort of patients before and after arthroplasty surgery. Forty-two patients with end stage knee osteoarthritis underwent instrumented three-dimensional kinematic and kinetic gait analysis in Dalhousie University's Dynamics of Human Motion (DOHM) Lab using a synchronized optoelectronic camera (OptoTrak) and inground force platform (AMTI) system immediately before and at approximately one year after (n=31) total knee arthroplasty surgery. Three-dimensional knee joint angles during gait were defined according to the joint coordinate system and net resultant external knee joint moments defined using an inverse dynamics approach. Key knee moment outcomes relevant to TKA were defined, and pearson's correlation analyses were used to examine associations between these moment outcomes with kinematic outcomes and patient demographics. We further explored potential multivariate linear regression models of the moment features with (uncorrelated) kinematic and demographic measures that were significant in univariate correlation analyses. Results of the univariate correlation analyses are shown in table 1. There were significant correlations for all moment metrics with kinematics, but more and stronger correlations were found for the sagittal plane moments than the frontal plane moments in general. Significant multiple linear regression models were defined for all moment outcomes (Table 2), with varying low to moderate levels of variability (R2) explained. Similar to the univariate results, sagittal plane moment models were stronger than frontal plane, with the KFM stance range having the strongest prediction model (R2 = 0.44, p < 0.0001). We found some moderate, significant correlations between gait kinetic outcomes and kinematic predictors, and we were able to define significant multivariate models. However, less than half of the variability in moment outcomes in this TKA population could be explained with kinematic and demographic variables, meaning that more information or input will be required to predict kinetic outcomes outside of a laboratory setting. In general, sagittal plane kinetic outcomes were better predicted than frontal plane. Future work will include examining the added value of the addition of wearable sensor (inertial and/or pressure outcomes) to kinematic data collections to improve our prediction of kinetic outcomes. For any figures or tables, please contact the authors directly.