The study compared thigh-shank and shank-foot coordination during gait before and after total knee arthroplasty (TKA) with controls (CTRL). Twenty-seven patients (male=15/female=12; age=63.2±6.9 years) were evaluated one month prior to and twelve months after surgery, and compared to 27 controls (male=14/female=13; age=62.2±4.3). The participants were outfitted with a full-body marker set. Gait speed (normalized by leg length) was calculated. The time series of the thigh, shank, and foot orientation in relation to the laboratory coordinate system were extracted. The coordination between the thigh-shank and shank-foot in the sagittal plane were calculated using a vector coding technique. The coupling angles were categorized into four coordination patterns. The stance phase was divided into thirds: early, mid, and late stance. The frequency of each pattern and gait speed were compared using a one-way ANOVA with a post-hoc Bonferroni correction. Walking speed and shank-foot coordination showed no differences between the groups. The thigh-shank showed differences. The pre-TKA group showed a more in-phase pattern compared to the CTRL group during early-stance. During mid-stance, the pre- and post-TKA presented a more in-phase pattern compared to the CTRL group. Regarding shank-foot coordination, the groups presented an in-phase and shank pattern, with more shank phase during mid-stance and more in-phase during late-stance. The pre-TKA group showed greater differences than the post-TKA compared to the controls. The more in-phase pattern in the pre- and post-TKA groups could relate to a reduced capacity for the thigh that leads the movement. During mid-stance in normal gait, the knee is extending, where the thigh and shank movements are in opposite directions. The in-phase results in the TKA groups indicate knee stiffness during the stance phase, which may relate to a muscular deficit or a gait strategy to reduce joint stress.
This study examined pre-operative measures to predict post-operative biomechanical outcomes in total knee arthroplasty (TKA) patients. Twenty-eight patients (female=12/male=16, age=63.6±6.9, BMI=29.9±7.4 kg/m2) with knee osteoarthritis scheduled to undergo TKA were included. All surgeries were performed by the same surgeon (GD) with a subvastus approach. Patients visited the gait lab within one-month prior to surgery and 12 months following surgery. At the gait lab, patients completed the knee injury and osteoarthritis outcome score (KOOS), a timed up and go (TUG), maximum knee flexion and extension strength evaluation, and a walking task. Variables of interest included the five KOOS sub-scores, TUG time, maximum knee flexion and extension strength normalized to body weight, walking speed, and peak knee biomechanics variables (flexion angle, abduction moment, power absorption). A Pearson's correlation was used to identify significantly correlated variables which were then inputted into a multiple regression. No assumption violations for the multiple regression existed for any variables. Pre-operative knee flexion and extension strength, TUG time, and age were used in the multiple regression. The multiple regression model statistically significantly predicted peak knee abduction moment, post-operative walking speed, and post-operative knee flexion strength. All four variables added statistically significantly to the prediction p<.05. Pre-operative KOOS values did not correlate with any biomechanical indicators of post-operative success. Age, pre-operative knee flexion and extension strength, and TUG times predicted peak knee abduction moment, which is associated with medial knee joint loading. These findings stress the importance of pre-surgery condition, as stronger individuals achieved better post-operative biomechanical outcomes. Additionally, younger patients had better outcomes, suggesting that surgeons should not delay surgery in younger patients. This delay in surgery may prevent patients from achieving optimal outcomes. Future studies should utilize a hierarchical multiple regression to identify which variables are most predictive.
