A multiple regression normalization approach to evaluation of gait in total knee arthroplasty patients
Introduction
Total knee arthroplasty (TKA) is the established treatment for end-stage knee osteoarthritis when conservative treatment options have been exhausted. In 2005, approximately 500,000 TKA procedures were performed in the United States alone at a cost exceeding $11 billion (Losina et al., 2009). It is estimated that close to 3.48 million TKAs will be performed annually by 2030 (Kurtz et al., 2007). While TKA has been shown to reduce pain, improve knee function, and increase quality of life (Ethgen et al., 2004, Hatfield et al., 2011), TKA patients are known to exhibit post-operative compensatory gait patterns to minimize joint loading and pain at the affected knee. This includes walking with less knee flexion (Mandeville et al., 2007, McClelland et al., 2011), a lower knee adduction moment (Alnahdi et al., 2011, McClelland et al., 2010), and a shorter swing phase in the affected leg (Andriacchi et al., 1982). In addition, lower ankle plantar–flexor power and higher hip flexor power has been observed in TKA subjects compared to aged-matched controls (Levinger et al., 2013).
Gait characteristics of an individual, including kinematics and net joint moments, are influenced by age, height, body mass, and gender (Moisio et al., 2003, Senden et al., 2012), as well as by variations in self-selected walking speed (Andriacchi et al., 1977, Lelas et al., 2003), including fluctuations within a single testing session (Benedetti et al., 2013, Yogev-Seligmann et al., 2008). Ultimately, variability in subject demographic factors increases dispersion of gait data and may limit capacity to discern some pathological movement patterns (Boyer et al., 2008, Hof, 1996). The dimensionless normalization (DS) equations of Hof (1996) are widely used in clinical gait analysis to normalize gait data to subject height and weight, and assume proportional scaling (Pierrynowski and Galea, 2001); however, the use of DS in normalizing spatiotemporal data, ground reaction forces (GRFs), and net joint moments has been shown to result in residual correlations between the normalized gait data and subject walking speed (Carty and Bennett, 2009, Lelas et al., 2003), body mass (Wannop et al., 2012), and gender (Boyer et al., 2008, Cho et al., 2004).
Statistical techniques, including use of linear (Lelas et al., 2003, O'Malley, 1996), multiple-linear (Lee et al., 1999, Macellari et al., 1999, Senden et al., 2012), and non-linear (Wannop et al., 2012) regression models, have been employed to scale gait data to subject anthropometry; however, these models do not account for variations in subject height, body mass, age, and gender and are rarely used to account for variations in subject walking speed. To minimize the influence of self-selected walking speed variations on gait data, subjects are often ‘speed-matched’ between groups, which can be difficult and time consuming to carry out in practice. Multiple-regression approaches to data normalization have been used to predict spatiotemporal gait data from anthropometrics and walking speed (Dixon et al., 2014); however, these approaches have received little attention to date and have not been used in models of joint angles, joint moments, and joint powers.
The aim of this study was twofold. First, to employ a multiple regression normalization (MR) method to minimize variation in subject joint angles, GRFs, net joint moments, and joint powers due to walking speed, body height, body mass, age, and gender in healthy adults; and second, to use this normalization approach to compare joint angles, GRFs, net joint moments, and net joint powers between TKA patients and healthy adults. We hypothesized that MR would reveal significant joint-level differences in angles, net moments, and powers in TKA patients and controls by eliminating effects of subject anthropometry and walking speed variations.
Section snippets
Experimental protocol
Gait data for 45 TKA patients and 31 aged-matched healthy controls were selected retrospectively from a gait database (Table 1). All TKA patients underwent unilateral arthroplasty by an experienced knee surgeon between August 2004 and July 2006 and received a posterior stabilized total knee replacement (Genesis-II PS, Smith and Nephew, Memphis, Tennessee, USA). Eighteen patients had joint replacement surgery on their left knee, while 27 had this surgery on their right knee. TKA patients were
Correlations
Using the raw, un-normalized gait data, peak kinetic data including GRFs, net joint moments, and net joint powers at the hip, knee, and ankle joints in the sagittal, coronal, and transverse planes were moderately to strongly correlated with subject body mass (0.4 < r < 0.9) (Table 2). Weak to moderate correlations (|r| < 0.4) were also observed between all raw gait data and age, height, gender, and walking speed. Normalizing gait data to subject body mass reduced correlations between peak net joint
Discussion
The objective of this study was to use a multiple regression normalization method to evaluate gait in TKA patients. After gait data normalization using MR, gait features observed in TKA subjects were in reasonable agreement with those of previous studies, including a lower peak vertical GRF (Lee et al., 1999, McClelland et al., 2010), lower peak knee adduction moment (Alnahdi et al., 2011, McClelland et al., 2010), and smaller peak knee extensor power (Fenner et al., 2014, Vahtrik et al., 2014)
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