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Muscle activations before and after total knee arthroplasty: An analysis of 11 different motor tasks
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Discussion: Even in the presence of unilateral disease, gait changes were present bilaterally and gait remained abnormal postoperatively in half of the individuals studied. The other joins provide useful discriminant data, especially power at the hip. Pre-operative gait function can be used to predict post-operative function in the majority of individuals. Additional rehabilitation may be required before and after surgery to optimise outcome following knee arthroplasty. Reference [1] Jones, et al. J Biomech 2006:2512–20.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.046
Session OS09 Osteoarthritis The relationship between joint alignment and biomechanics in total knee arthroplasty: An in-vivo analysis A. Metcalfe 1,∗ , J. Madete 2 , D. Williams 1 , P. Biggs 1 , G. Whatling 1 , P. Kempshall 3 , M. Forster 4 , C. Holt 1 1
School of Engineering, Cardiff University, Cardiff, United Kingdom 2 Kenyatta University, Nairobi, Nairobi, Kenya 3 Gloucestershire Hospitals NHS Foundation Trust, Gloucester, United Kingdom 4 University Hospital of Wales, Cardiff, United Kingdom Research question: The aim of this study was to assess the relationship between joint alignment, moments and objective gait function in a cohort of patients known to have high rates of joint mal-alignment. Introduction: Total knee replacement is a common operation, with approximately 80,000 performed in the UK every year. The optimum alignment for a total knee replacement and acceptable levels of error remains a source of debate, but the importance of mal-alignment in terms of the effect on biomechanics and physical function is poorly understood. Materials and methods: Twenty-seven patients with 31 Kinemax (Stryker) Total Knee Replacements were recruited prospectively from a previously reported cohort with high rates of component mal-position. Long-leg radiographs and low radiation CT scans were used to define component alignment and axial rotation. Gait analysis was performed for level gait using an 8 camera Qualysis Pro-reflex (Qualysis, Sweden) system with 2 Bertec force plates built into the floor and was processed using Visual 3D (C-motion, USA). Objective function was defined using a statistical classification technique based on uncertain reasoning, previously developed for studying function in knee replacement patients [1]. Principle component analysis was used to extract the maximum amount of useful information from the kinematic and kinetic waveforms. A classifier was trained with 20 subjects with severe knee OA and 20 healthy subjects with gait data collected in the same manner, allowing new data to be plotted on a spectrum from severe OA to fully healthy function, with uncertainty also considered in the analysis. Correlations were assessed using simple linear regression, and statistical comparisons were made between groups using Mann-Whitney U tests as normality of the data could not be assumed with the small group sizes. Results: The mean age was 74 (range 60–89), mean Oxford score was 35 (13–47) and mean KOOS score was 72 (15–98). Mean
S23
Hip-Knee-Ankle measurement was 1.1◦ varus (10◦ varus to 9.5◦ valgus) and mean femoral and tibial rotation was 1◦ internal and 4◦ external. Peak coronal plane knee moments were closely related to HKA alignment (R2 = 0.55, p < 0.01), with high moments associated with varus alignment. Objective function was worse in implants aligned in valgus >2◦ (n = 8) compared to those within 2◦ of neutral (n = 10, Mann–Whitney-U p = 0.021), whereas implants in varus >2◦ (n = 13) had the same function as those within 2◦ of neutral (p = 0.74). Joint obliquity, tibial slope and CT rotational profile were not related to gait function. Discussion: Varus alignment results in potentially damaging joint moments, but consistently poor function is seen when implants are aligned in Valgus. Further work and large cohorts are required to define the precise alignment thresholds needed to optimise longevity and function in total knee replacement. http://dx.doi.org/10.1016/j.gaitpost.2015.06.047
Session OS09 Osteoarthritis Muscle activations before and after total knee arthroplasty: An analysis of 11 different motor tasks L. Scheys 1,∗ , B. Callewaert 2 , M. Vanslembrouck 3 , J. Vermulst 3 , K. Desloovere 2 , H. Vandenneucker 4 1
Lab for Orthopaedic Research, University Hosp. Leuven/KU Leuven, European Centre for Knee Research – Smith&Nephew, Pellenberg, Belgium 2 K.U. Leuven, C-MAL – Department of Rehabilitation Sciences, Pellenberg, Belgium 3 Lab for Orthopaedic Research, University Hosp. Leuven/KU Leuven, Pellenberg, Belgium 4 Dep. of Orthopaedics, University Hosp. Leuven – campus Pellenberg, Pellenberg, Belgium Research question: To what extent do muscle activation patterns normalize after a total knee arthroplasty (TKA) and is this consistent over multiple motor tasks? Introduction: Subjects with end-stage osteoarthritis are known to demonstrate significant kinematic differences when compared to healthy controls during specific motor tasks. Furthermore it has been reported that following TKA some of these differences fade out or even fully disappear. However, very few studies simultaneously looked at muscle activations. Finally, in most cases only one specific motor task – mostly gait – was analyzed. This study aims to improve our clinical insight in knee replacement surgery by comparing pre- and post-operative muscle activation patterns in a group of knee osteoarthritic patient during eleven daily-live motor tasks. Furthermore, through an additional comparison with healthy controls we want to investigate to what extent muscle activation patterns tent to normalize following knee replacement surgery. Materials and methods: Ten subjects diagnosed with unilateral symptomatic primary knee osteoarthritis participated in this study (Sex: 5 M, 5F; 5 right-, 5 left-affected; Age: 62.8 ± 7.3y; Height: 1.72 ± 0.08m; BMI: 26.2 ± 1.7). Prior to surgery, 3D motion analysis was performed in each subject while performing 11 different motor tasks with 3 repetitions each: walking, walking followed by a crossover turn and sidestep turn, ascent onto a step, descent off a step, descent followed by a crossover and sidestep turn, mild and maximum squat, chair rise and lunge. Simultaneously, EMG data was collected using a 16 channel wireless EMG system and Ag/AgCl electrodes placed on the centre of the muscle belly of selected lower
S24
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Fig. 1. Ensemble averages of normalized rectus femoris activations (one SD error bounds).
limb muscles. 12 Months post-surgery, all measurements were repeated in a subset of 7 subjects. In addition, data from 10 healthy control subjects (matched on age, sex and BMI) were included from the gait labs? reference database. EMG was processed as in [1]. Data analysis was performed using specific in-house developed analysis software. Results: Ensemble averages of normalized rectus femoris activations (one SD error bounds). In comparison with both the pre-TKA condition as with matched healthy controls, post-TKA subjects demonstrated clear differences in terms of muscle activation patterns. Furthermore, these differences were not always consistent throughout the motor tasks analyzed. Fig. 1 demonstrates this finding for the rectus femoris muscle. Discussion: The motor-task dependent aberrant muscle activation profile in post-TKA subjects demonstrates the need to include multiple motor tasks when studying knee joint functionality following TKA. Reference [1] Davidson BS, et al. J Electromyogr Kinesiol 2013;23.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.048
Session OS09 Osteoarthritis Quantification of gait deviations in alkaptonuria: A comparison of joint angles versus marker coordinates G. Barton 1,∗ , M. Hawken 1 , S. King 1 , M. Robinson 1 , L. Ranganath 2 1
Liverpool John Moores University, Research Institute for Sport and Exercise Sciences, Liverpool, United Kingdom 2 National Alkaptonuria Centre, Royal Liverpool University Hospital, Liverpool, United Kingdom
Research question: Are marker coordinates better descriptors of gait deviation than joint angles in alkaptonuria? Introduction: Movement of body segments during gait is commonly represented by 3D joint angles calculated from the 3D movement of markers attached to segments, probably due to their perceived anatomical relevance. Using neural networks the Movement Deviation Profile [1] (MDP) provides a measure of the
distance of gait from normality. Although the MDP was originally validated using joint angles, it can also characterise the deviation using 3D marker coordinates. Alkaptonuria [2] (AKU) is a rare inherited metabolic disease leading to early osteoarthritis and deterioration of gait. The aim of this study was to determine whether the use of 3D marker coordinates can capture gait deviation better than 3D joint angles in a large cohort of patients with AKU. Materials and methods: Spatial movement of markers attached to the skin according to the Davis model [3] was recorded during walking in 39 AKU patients and 10 controls (age 46.5 ± 12.7 and 34.2 ± 13.1 years respectively). Gait was quantified both by Angles (ankle dorsiflexion and abduction, knee flexion, hip flexion and abduction, pelvic rotation and obliquity and tilt) and by Markers (standardised X, Y and Z coordinates of the 15 markers, [4,5]) derived from each person’s left gait cycle. Angles of controls were passed to the self organising neural network underlying the MDP program for training, and then Angles of the controls and patients were presented to generate MDPmean values for each control and patient as single number measures of gait deviation. In a second run Marker data were used to produce MDPmean values for the same controls and patients. Pearson’s correlation was performed to determine relationships between MDPmean derived from Angles and Markers. Results: Graphical plots of MDPmean against age using Markers showed a sigmoid profile [4], while MDPmean derived from Angles showed a less obvious pattern. Within the AKU group, a positive moderate relationship was found between MDPmean from Markers and Angles (R = 0.461, P = 0.003). Discussion: The moderate correlation between gait deviations calculated using Angles and Markers suggests that the two representations of gait contain related but different information. A possible explanation of the difference is the arbitrary choice of a particular Euler rotation sequence for calculating Angles. Even without access to a gold standard measure of gait deviation, the sigmoid pattern of gait deterioration derived from Markers shows a better match with AKU’s natural progression [2], supporting the use of Markers instead of Angles to quantify gait deviations in AKU with the MDP. References [1] [2] [3] [4] [5]
Barton, et al. Hum Mov Sci 2012. Ranganath, Cox. J Inherit Metab Dis 2011. Davis, et al. Hum Mov Sci 1991. Barton, et al. JIMD Rep 2015. Federolf, et al. J Biomech 2013.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.049
Discussion: Even in the presence of unilateral disease, gait changes were present bilaterally and gait remained abnormal postoperatively in half of the individuals studied. The other joins provide useful discriminant data, especially power at the hip. Pre-operative gait function can be used to predict post-operative function in the majority of individuals. Additional rehabilitation may be required before and after surgery to optimise outcome following knee arthroplasty. Reference [1] Jones, et al. J Biomech 2006:2512–20.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.046
Session OS09 Osteoarthritis The relationship between joint alignment and biomechanics in total knee arthroplasty: An in-vivo analysis A. Metcalfe 1,∗ , J. Madete 2 , D. Williams 1 , P. Biggs 1 , G. Whatling 1 , P. Kempshall 3 , M. Forster 4 , C. Holt 1 1
School of Engineering, Cardiff University, Cardiff, United Kingdom 2 Kenyatta University, Nairobi, Nairobi, Kenya 3 Gloucestershire Hospitals NHS Foundation Trust, Gloucester, United Kingdom 4 University Hospital of Wales, Cardiff, United Kingdom Research question: The aim of this study was to assess the relationship between joint alignment, moments and objective gait function in a cohort of patients known to have high rates of joint mal-alignment. Introduction: Total knee replacement is a common operation, with approximately 80,000 performed in the UK every year. The optimum alignment for a total knee replacement and acceptable levels of error remains a source of debate, but the importance of mal-alignment in terms of the effect on biomechanics and physical function is poorly understood. Materials and methods: Twenty-seven patients with 31 Kinemax (Stryker) Total Knee Replacements were recruited prospectively from a previously reported cohort with high rates of component mal-position. Long-leg radiographs and low radiation CT scans were used to define component alignment and axial rotation. Gait analysis was performed for level gait using an 8 camera Qualysis Pro-reflex (Qualysis, Sweden) system with 2 Bertec force plates built into the floor and was processed using Visual 3D (C-motion, USA). Objective function was defined using a statistical classification technique based on uncertain reasoning, previously developed for studying function in knee replacement patients [1]. Principle component analysis was used to extract the maximum amount of useful information from the kinematic and kinetic waveforms. A classifier was trained with 20 subjects with severe knee OA and 20 healthy subjects with gait data collected in the same manner, allowing new data to be plotted on a spectrum from severe OA to fully healthy function, with uncertainty also considered in the analysis. Correlations were assessed using simple linear regression, and statistical comparisons were made between groups using Mann-Whitney U tests as normality of the data could not be assumed with the small group sizes. Results: The mean age was 74 (range 60–89), mean Oxford score was 35 (13–47) and mean KOOS score was 72 (15–98). Mean
S23
Hip-Knee-Ankle measurement was 1.1◦ varus (10◦ varus to 9.5◦ valgus) and mean femoral and tibial rotation was 1◦ internal and 4◦ external. Peak coronal plane knee moments were closely related to HKA alignment (R2 = 0.55, p < 0.01), with high moments associated with varus alignment. Objective function was worse in implants aligned in valgus >2◦ (n = 8) compared to those within 2◦ of neutral (n = 10, Mann–Whitney-U p = 0.021), whereas implants in varus >2◦ (n = 13) had the same function as those within 2◦ of neutral (p = 0.74). Joint obliquity, tibial slope and CT rotational profile were not related to gait function. Discussion: Varus alignment results in potentially damaging joint moments, but consistently poor function is seen when implants are aligned in Valgus. Further work and large cohorts are required to define the precise alignment thresholds needed to optimise longevity and function in total knee replacement. http://dx.doi.org/10.1016/j.gaitpost.2015.06.047
Session OS09 Osteoarthritis Muscle activations before and after total knee arthroplasty: An analysis of 11 different motor tasks L. Scheys 1,∗ , B. Callewaert 2 , M. Vanslembrouck 3 , J. Vermulst 3 , K. Desloovere 2 , H. Vandenneucker 4 1
Lab for Orthopaedic Research, University Hosp. Leuven/KU Leuven, European Centre for Knee Research – Smith&Nephew, Pellenberg, Belgium 2 K.U. Leuven, C-MAL – Department of Rehabilitation Sciences, Pellenberg, Belgium 3 Lab for Orthopaedic Research, University Hosp. Leuven/KU Leuven, Pellenberg, Belgium 4 Dep. of Orthopaedics, University Hosp. Leuven – campus Pellenberg, Pellenberg, Belgium Research question: To what extent do muscle activation patterns normalize after a total knee arthroplasty (TKA) and is this consistent over multiple motor tasks? Introduction: Subjects with end-stage osteoarthritis are known to demonstrate significant kinematic differences when compared to healthy controls during specific motor tasks. Furthermore it has been reported that following TKA some of these differences fade out or even fully disappear. However, very few studies simultaneously looked at muscle activations. Finally, in most cases only one specific motor task – mostly gait – was analyzed. This study aims to improve our clinical insight in knee replacement surgery by comparing pre- and post-operative muscle activation patterns in a group of knee osteoarthritic patient during eleven daily-live motor tasks. Furthermore, through an additional comparison with healthy controls we want to investigate to what extent muscle activation patterns tent to normalize following knee replacement surgery. Materials and methods: Ten subjects diagnosed with unilateral symptomatic primary knee osteoarthritis participated in this study (Sex: 5 M, 5F; 5 right-, 5 left-affected; Age: 62.8 ± 7.3y; Height: 1.72 ± 0.08m; BMI: 26.2 ± 1.7). Prior to surgery, 3D motion analysis was performed in each subject while performing 11 different motor tasks with 3 repetitions each: walking, walking followed by a crossover turn and sidestep turn, ascent onto a step, descent off a step, descent followed by a crossover and sidestep turn, mild and maximum squat, chair rise and lunge. Simultaneously, EMG data was collected using a 16 channel wireless EMG system and Ag/AgCl electrodes placed on the centre of the muscle belly of selected lower
S24
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Fig. 1. Ensemble averages of normalized rectus femoris activations (one SD error bounds).
limb muscles. 12 Months post-surgery, all measurements were repeated in a subset of 7 subjects. In addition, data from 10 healthy control subjects (matched on age, sex and BMI) were included from the gait labs? reference database. EMG was processed as in [1]. Data analysis was performed using specific in-house developed analysis software. Results: Ensemble averages of normalized rectus femoris activations (one SD error bounds). In comparison with both the pre-TKA condition as with matched healthy controls, post-TKA subjects demonstrated clear differences in terms of muscle activation patterns. Furthermore, these differences were not always consistent throughout the motor tasks analyzed. Fig. 1 demonstrates this finding for the rectus femoris muscle. Discussion: The motor-task dependent aberrant muscle activation profile in post-TKA subjects demonstrates the need to include multiple motor tasks when studying knee joint functionality following TKA. Reference [1] Davidson BS, et al. J Electromyogr Kinesiol 2013;23.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.048
Session OS09 Osteoarthritis Quantification of gait deviations in alkaptonuria: A comparison of joint angles versus marker coordinates G. Barton 1,∗ , M. Hawken 1 , S. King 1 , M. Robinson 1 , L. Ranganath 2 1
Liverpool John Moores University, Research Institute for Sport and Exercise Sciences, Liverpool, United Kingdom 2 National Alkaptonuria Centre, Royal Liverpool University Hospital, Liverpool, United Kingdom
Research question: Are marker coordinates better descriptors of gait deviation than joint angles in alkaptonuria? Introduction: Movement of body segments during gait is commonly represented by 3D joint angles calculated from the 3D movement of markers attached to segments, probably due to their perceived anatomical relevance. Using neural networks the Movement Deviation Profile [1] (MDP) provides a measure of the
distance of gait from normality. Although the MDP was originally validated using joint angles, it can also characterise the deviation using 3D marker coordinates. Alkaptonuria [2] (AKU) is a rare inherited metabolic disease leading to early osteoarthritis and deterioration of gait. The aim of this study was to determine whether the use of 3D marker coordinates can capture gait deviation better than 3D joint angles in a large cohort of patients with AKU. Materials and methods: Spatial movement of markers attached to the skin according to the Davis model [3] was recorded during walking in 39 AKU patients and 10 controls (age 46.5 ± 12.7 and 34.2 ± 13.1 years respectively). Gait was quantified both by Angles (ankle dorsiflexion and abduction, knee flexion, hip flexion and abduction, pelvic rotation and obliquity and tilt) and by Markers (standardised X, Y and Z coordinates of the 15 markers, [4,5]) derived from each person’s left gait cycle. Angles of controls were passed to the self organising neural network underlying the MDP program for training, and then Angles of the controls and patients were presented to generate MDPmean values for each control and patient as single number measures of gait deviation. In a second run Marker data were used to produce MDPmean values for the same controls and patients. Pearson’s correlation was performed to determine relationships between MDPmean derived from Angles and Markers. Results: Graphical plots of MDPmean against age using Markers showed a sigmoid profile [4], while MDPmean derived from Angles showed a less obvious pattern. Within the AKU group, a positive moderate relationship was found between MDPmean from Markers and Angles (R = 0.461, P = 0.003). Discussion: The moderate correlation between gait deviations calculated using Angles and Markers suggests that the two representations of gait contain related but different information. A possible explanation of the difference is the arbitrary choice of a particular Euler rotation sequence for calculating Angles. Even without access to a gold standard measure of gait deviation, the sigmoid pattern of gait deterioration derived from Markers shows a better match with AKU’s natural progression [2], supporting the use of Markers instead of Angles to quantify gait deviations in AKU with the MDP. References [1] [2] [3] [4] [5]
Barton, et al. Hum Mov Sci 2012. Ranganath, Cox. J Inherit Metab Dis 2011. Davis, et al. Hum Mov Sci 1991. Barton, et al. JIMD Rep 2015. Federolf, et al. J Biomech 2013.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.049