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Comparison of total hip arthroplasty surgical approaches by Principal Component Analysis
Journal of Biomechanics 45 (2012) 2109–2115
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Comparison of total hip arthroplasty surgical approaches by Principal Component Analysis Giulia Mantovani a, Mario Lamontagne a,n, Daniel Varin a, Giuliano G. Cerulli b, Paul E. Beaule´ c a
School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada Let People Move Research Institute, Perugia/Arezzo, Italy c Division of Orthopedic Surgery, University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada b
a r t i c l e i n f o
abstract
Article history: Accepted 24 May 2012
Gait adaptations are persistent after total hip arthroplasty and can depend on the type of surgery. This study focused on two surgical approaches: anterior and lateral. To analyze gait adaptations, biomechanical analyses usually employ an a priori selection of the parameters that leads to incomplete or redundant information. In contrast, Principal Component Analysis (PCA) provides an efficient transformation of the dataset by automatically identifying the major sources of variability. The purpose of this study was to investigate differences in level-walking among three groups of participants using PCA: patients undergoing an anterior surgical approach, patients undergoing a lateral surgical approach, and healthy controls. Biomechanical descriptions of the extracted principal components aided in the interpretation of the statistically significant results obtained from multivariate analysis of covariance (MANCOVA) tests. A point system was introduced to summarize the results and guide the interpretation. PCA captured reduced magnitude in sagittal and frontal moments in the anterior approach group, and reduced sagittal peaks angle in the lateral group, as previously found with traditional analyses. PCA also identified significant pattern delays in the anterior group, unnoticed in previous studies. In conclusion, neither surgical approach restored normal gait functionality because lower extremity kinetics and kinematics alterations persisted at 300-day follow-up after the surgery, regardless of the technique. & 2012 Elsevier Ltd. All rights reserved.
Keywords: Principal Component Analysis Total hip arthroscopy Variability Lateral surgical approach Anterior surgical approach
1. Introduction Despite significant improvements in gait patterns, gait adaptations persist after total hip arthroplasty (THA) in both short and long term follow-up (Bennett et al., 2009; Foucher et al., 2007). The choice of surgery can influence postoperative gait; with this in mind, this study compares two different surgical approaches: the modified direct lateral approach (Mulliken et al., 1998) and the single-incision anterior approach (Matta et al., 2005). The lateral approach involves the detachment of the gluteus medius and minimus from the greater trochanter, leading to abductor dysfunction and a postoperative limp (Masonis and Bourne, 2002). Conversely, the anterior approach spares the stabilizer structures of the pelvis (Matta et al., 2005), with a reduced risk of dislocation (Nakata et al., 2009; Sariali et al., 2008). Biomechanical investigations have slightly favored the anterior approach in short-term follow-up (Klausmeier et al., 2010; Restrepo et al., 2010) but show the persistence of abnormalities in long-term follow-up,
n
Corresponding author. Tel.: þ1 613 562 5800x4258; fax: þ1 613 562 5328. E-mail address: [email protected] (M. Lamontagne).
0021-9290/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jbiomech.2012.05.041
regardless of the type of surgery (gait analysis studies: Beaulieu et al., 2010; Madsen et al., 2004; Martin et al., 2011; Meneghini et al., 2008; Queen et al., 2011; and stairs climbing analysis: Lamontagne et al., 2011). In particular, at a 16-week follow-up, Klausmeier et al. (2010) found a significant reduction in the frontal and sagittal plane hip range of motion and in the frontal plane peak moment in both anterior and anterolateral approaches respect to controls. No secondary effects on the other joints were analyzed even though it has been shown that neighboring joints are also affected by primary alterations on the hip (Perron et al., 2000). In biomechanical evaluations, parameters extracted from data usually include peaks and ranges of variation of the kinetics and kinematics variables (Lamontagne et al., 2009). These parameters are selected a priori, potentially leading to incomplete or redundant information (Mantovani et al., 2011). Principal Component Analysis (PCA) is adopted to derive an efficient representation of the original dataset by retaining potentially valuable temporal information (Chau, 2001), and overcoming the problem of the a priori parameters selection (O’Connor and Bottum, 2009). PCA can detect significant differences among groups of participants, and the differences can be correlated to specific pathological
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conditions by introducing an interpretation of the newly transformed waveforms (Deluzio and Astephen, 2007; Dona et al., 2009; Landry et al., 2007; Wrigley et al., 2006). Thus, our purpose was to analyze the kinetics and kinematics of the lower-extremity joints during walking: the study compared healthy controls and THA patients operated with an anterior or lateral surgical approach, at an average of te10-months follow-up, by using PCA. A point system based on PCA outcomes was also introduced to simplify the interpretation of results. Since previous traditional analyses identified differences mainly in the frontal and sagittal hip ranges of motion and peak moments (Klausmeier et al., 2010; Lamontagne et al., 2011), we hypothesized that PCA would identify magnitude and amplitude as main sources of variability in the same variables (Deluzio and Astephen, 2007; Landry et al., 2007). It was also hypothesized that magnitude and amplitude of frontal and sagittal hip angles and moments would be reduced in THA patients compared to the controls. We also expected to show differences not detected with previous a priori parameter selection, such as differences in temporal and shape characteristics (Deluzio and Astephen, 2007; Landry et al., 2007; Wrigley et al., 2006). Lastly, significant secondary effects in the neighboring joints directly linked to the primary alterations of the hip variables were expected (Perron et al., 2000).
2. Methods 2.1. Subjects and protocol Sixty participants were recruited and divided into three groups matched for age and body mass index (Table 1). Twenty patients had THA by means of anterior approach (ANT), 20 by lateral approach (LAT) and 20 were healthy controls (CON). All patients were recruited approximately 300 days after their surgery (Table 1), had undergone primary unilateral THA for reasons other than infection or fracture, and were not suffering from any other known condition that could alter their gait. The type of surgical approach was not randomized; however, the patients were not selected prior to surgery, hence there was no bias concerning the choice of the surgery. Three different physicians performed the surgeries and two different implants were used (Table 1). The institution’s research ethics board approved the study and the patients provided written informed consent. Participants performed three level-walking trials at a self-selected pace, which was preferred over imposing a pre-defined walking speed, to create experimental conditions as close as possible to everyday situations encountered by the patients. The statistical analysis revealed that walking speed was not significantly different among the three groups. Three-dimensional trajectories of a modified Helen Hayes marker-set (Lamontagne et al., 2011), and ground reaction forces were collected with a 9-camera motion analysis system (Vicon MX-13) and two Kistler force plates. Through inverse kinematics and kinetics models, joint angles and moments were calculated, and moments were normalized by body weight (Lamontagne et al., 2011). The transverse plane variables were not included in the analysis because of the high inter-subject variability reported for this plane (McGinley et al., 2009). In summary, a total of 12 waveforms were used for the PCA: the
sagittal and frontal angles and moments for the hip, knee and ankle joints. The waveforms were time-normalized to the gait cycle percentage using a cubic spline function. The region of interest for angles was from foot-strike to foot-strike of the affected side, while for moments was from foot-strike to toe-off of the affected side. Three trials for each subject were averaged.
2.2. PCA: background and application to gait analysis PCA consists of an orthogonal transformation that converts a set of observations of m correlated variables X ¼ ½x1 ,x2 ,. . .,xm T into a set of uncorrelated ones (i.e., principal components or PCs), Y ¼ ½y1 ,y2 ,. . .,yd T , with d o m. The transformation is defined by the equation Y ¼ AT X, where the columns of the matrix Amd ¼ ½A1 ,A2 ,. . .,Ad are the first d eigenvectors (sorted from the largest eigenvalue, li) of the covariance matrix of X. In doing this, the new dataset Y contains the majority of the information of the original dataset, which can be represented by only d components instead of m. Y values are called scores, and the columns of A are the loadings (Jolliffe, 2002). In gait analysis, PCA can be employed to identify the dominant sources of variability among subjects with respect to a specific variable (O’Connor and Bottum, 2009). In this study, a PCA was performed for each variable: the correlated variables were the time points m ¼ 101 with p observations corresponding to the number of participants (in this study p ¼ 60). The transformation equation was Y ¼ AT ðXX Þ, where X 101x60 was the matrix of horizontally concatenated waveforms and X 101x60 ¼ ½x 1 ,x 2 ,. . .,x 101 T was the matrix of the inter-participant averages (Dona et al., 2009). The means were subtracted to make PCA capture the variability among subjects and not the absolute values. The matrix Y dx60 contained the scores, and high scores participants signified high correlation with the specific source of variability identified by that PC. d was the number of retained PCs and was defined based on the cumulative variability criterion (Jolliffe, 2002). The variability content of a PC was defined as the percentage P vari % ¼ li = m j ¼ 1 lj , and the cumulative variability was given by cumvar i % ¼ Pm Pi j ¼ 1 lj = j ¼ 1 lj . The criterion suggested to stop when cumvar i % was higher than a certain threshold, which we set at 90%. This threshold was also used by Lee et al. (2009) and it can be considered a good compromise between the necessity of retaining enough information content and the interpretability of the PCs that becomes more difficult at higher PC numbers. To help the interpretation of the PCs, they were represented as suggested by Ramsay and Silverman (2002), who portrayed the effect of a PC about the mean curve of the original signal by adding (þ) and subtracting ( ) a multiple Q of the PC loadings to the mean curve itself. Depending on the behavior of mean waveforms, they suggested the possibility of subjectively adjusting the Q value. Therefore, we used the 5th and 95th percentiles of the PC score distributions as Q values for the low ( ) and high (þ ) curves respectively (Figs. 1 and 2), because the single PC contribution to the total waveform was more clearly represented.
2.3. Statistics For each of the 12 original variables, the main effect of the surgical approach (independent variable) was analyzed in a one-way multivariate analysis of covariance (MANCOVA), which used PCs scores as dependent variables. Using gender as covariate, possible interactions between surgical approach and the unbalanced female/male ratio among groups was controlled. Post-hoc analyses verified the specific pairwise comparisons (LAT-CON, ANT-CON, LAT-ANT), and Bonferroni’s correction adjusted the significance level to a ¼ 0.017, preventing for type I errors. The homogeneity-of-regression assumption was first tested to
Table 1 The columns of the table report: group name, gender ratio (number of female/number of male), age, body mass index (BMI), time of testing after surgery, walking speed (all expressed in terms of mean and standard deviations in round brackets), list of surgeons who performed the operations (initials and number of operated subjects in round brackets) and type of prosthetic implants (name of prosthesis and number of subjects who got it). Group
Female/male
Age (years)
BMI (kg/m2)
Time after THA (days)
Walking speed (m/s)a
CON LAT
10/10 10/10
63.5(4.4) 66.2(6.7)
24.9(3.5) 27.2(5.0)
323(79)
1.29(0.15) 1.14(0.21)
Surgeons
Type of implant (N)
PB (9)
Stryker Accolade stem with Trident acetabulum (13)
PK (6) RF (5) ANT
14/6
60.5(6.0)
28.5(4.9)
291(114)
1.31(0.15)
PB (17) PK (3)
a
The result from the ANOVA test on the walking speed is not statistically significant (p ¼0.051).
Wright Medical Profemur stem with Conserve Plus acetabulum (7) Stryker Accolade stem with Trident acetabulum (5) Wright Medical Profemur stem with either Lineage (8), Conserve Plus (6) or Dynasty (1) acetabulum
G. Mantovani et al. / Journal of Biomechanics 45 (2012) 2109–2115
Fig. 1. The average hip sagittal angle of the complete dataset is plotted (black solid curve) together with the PC2 (A) and PC3 (B) effects, respectively amplitude and time shift. The variability explained by the PCs is also reported as percentage. The PCs are represented as perturbation of the mean of the original signal, by adding (þhigh) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The scatter plot (C) reports the scores distribution relative to the PCs of interest, where every participant is represented by a marker. AMP¼ amplitude, TS¼time shift, CON¼ control, LAT¼ lateral, ANT¼ anterior. exclude any significant interaction between gender and surgical approach. Preliminary assumption testing was conducted to check for normality, linearity, outliers, and homogeneity of variance–covariance and multicollinearity, with no severe violations noted. Walking speed differences among the groups were tested by an ANOVA (a ¼ 0.05) to exclude possible influences on the kinematics and kinetics analyses, and no significant differences were found (Table 1). A point system was created based on a similar approach proposed by Deluzio et al. (1997) to summarize the statistical results: each statistically significant difference from the pairwise comparisons was worth one point. If no significant difference was found, no points were added. Notably, a low score for a group indicated a higher similarity to the CON group. The differences in the point system score do not necessarily provide a complete understanding of the results but are only used as reference for clinical interpretation. The calculations for the PCA were done using Matlabs (version R2010a, The MathWorks, Natik, MA, USA), while the statistical analysis was carried out with SPSS version 19 (SPSS Inc., Chicago, IL, USA).
2.4. Biomechanical interpretation of the principal components PCs were divided into four main sources of variability that could be interpreted as biomechanical parameters: amplitude (AMP), magnitude (MAG), time shift (TS) and pattern (PAT). AMP identified a variation in the peaks’ amplitude of the waveforms (Fig. 1A). The presence of MAG indicated a large variability in the averages of the original dataset (i.e., an offset among the original trials), which produced a vertical shift of the curves (Fig. 2A). TS captured a change in the timing of the waveform events, which produced a horizontal shift (Fig. 1B). PAT identified specific changes in the waveform patterns that do not fall into the other categories (Fig. 2B). The same PC can be well interpreted as more than one source of variability at the time (for example, AMP and TS). In the proposed point system, the points gained by LAT and ANT were sorted according to sources of variability and type of original variable (angle and moment), linking statistical results and functional meanings of the variables. In case one PC was interpreted with two sources of variability and was statistically
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Fig. 2. The average hip frontal moment of the complete dataset is plotted (black solid curve) together with the PC1 (A) and PC2 (B) effects, respectively magnitude and pattern. The variability explained by the PCs is also reported as percentage. The PCs are represented as perturbation of the mean of the original signal, by adding (þ high) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The scatter plot (C) reports the scores distribution relative to the PCs of interest, where every participant is represented by a marker. MAG¼ magnitude, PAT¼ pattern, CON¼ control, LAT¼ lateral, ANT¼anterior. significant, the point system would only add half a point to conserve the total score.
3. Results Beside PC1 and PC3 of frontal knee moment, which were not considered for the rest of the analysis, the homogeneity-ofregression assumption was not significant for any variable (Table s1). Therefore, there were no significant interactions between the covariate (gender) and the independent variable (group), which justifies the use of the MANCOVA test. PCs’ variability content, post-hoc analysis p-values and sources of variability labels are reported only when there was statistical significance (Table 2). A complete table that reports scores’ averages and standard deviations, and the non-significant results can be found in the supplementary material (Table s1). The statistical results were summarized in the point system (Fig. 3); TOT (total) is the sum of the scores from different sources of variability and showed that THA patients maintained significant differences in kinematic and kinetic variables, regardless of the surgical approach. The two approaches did not always result in similar gait alterations; differences were observed between the two surgical groups, particularly in the kinetic variables. For LAT, the majority of the significant differences concerned peak amplitude, while the differences for ANT were mainly given by magnitude and time shift.
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Table 2 The table reports the results of the post-hoc analyses following the MANCOVA tests on the PC scores. The ‘Source of variability’ column reports the labels describing the identified source of variability of the PCs: MAG (magnitude), AMP (amplitude), TS (time shift) and PAT (pattern). The post-hoc analysis results indicate the statistical different pairwise comparisons. Variables
PC
Sources of variability
Post-hoc Analysisa LAT–CON
Hip angle (sagittal)
Hip angle (Frontal) Knee angle (sagittal) Ankle angle (sagittal) Hip moment (sagittal) Hip moment (frontal) Knee moment (sagittal) Ankle moment (sagittal)
PC1 PC2 PC3 PC1 PC2 PC3 PC1 PC2 PC1 PC1 PC2 PC1 PC3
AMP AMP TS MAG AMP þTS AMP þTS MAG AMP MAG MAG AMP MAG þTS TS
ANT–CON
0.001 (LAT o CON) o0.001 (LATo CON) 0.009 (ANT 4CON) 0.004 (ANT o CON) 0.001 (LATo CON) o0.001 (LAT4CON) 0.001 (LAT 4CON) 0.013 (LAT 4CON)
LAT–ANT
0.002 (LATo ANT) 0.011 (LATo ANT) o0.001 (LAToANT)
o0.001 (ANT 4CON)
o0.001 (ANT 4CON) o0.001 (ANT oCON) o0.001 (LAT4CON) o0.001 (ANT oCON) 0.008 (ANT 4CON)
0.010 (LAT4ANT) o0.001 (LAToANT) o0.001 (LAT4ANT) 0.007 (LAT4ANT) o0.001 (LAT4ANT) 0.005 (LATo ANT)
Direction of the difference between the mean scores is reported in brackets. a
statistically significant, Bonferroni’s correction a ¼0.017.
and ANT and the slightly delayed pattern for ANT (Fig. 4E and F). PC1 of the sagittal ankle angle captured the more dorsiflexed LAT curve (Fig. 4G), which was significantly different from CON. PC2 of hip sagittal moment captured the decreased amplitude of the peaks in LAT (Fig. 5A). PC1 of the hip frontal moment captured the global magnitude of the waveforms, with ANT showing a significantly less adducted moment versus CON and LAT (Fig. 5B and C). PC1 and PC2 of the sagittal knee moment captured the difference in magnitude and amplitude among the groups (Fig. 5C and D), reporting a significantly more extended curve for ANT and lower peaks for LAT. Lastly, PC1 of the sagittal flexion moment of the ankle joint captured the higher magnitude of the ANT group (Fig. 5E), statistically different from LAT and CON, while PC3 identified the time shift in the ANT curve (Fig. 5F). Fig. 3. The graphs summarize the results from Table 2 according to the point system that assigns 1 point to each statistically significant difference and 0.5 point if two sources of variability are identified for the same PC. The results are sorted according to the type of original variable and source of variability. The higher the column, the less similar are the lateral or anterior groups to the control group. MAG¼ magnitude, AMP¼ amplitude, TS ¼ time shift, PAT ¼pattern, CON¼ control, LAT¼ lateral, ANT ¼anterior.
At the hip sagittal angle, PC1 captured the reduced amplitude of LAT (Fig. 4A), in the intervals 0–35% and 70–100%, with low curve being less flexed than high curve. Consequently, a participant with a negative score for this PC had a less flexed hip angle with respect to the controls. In this interval, LAT was closer to the low curve and CON to the high curve and, in fact, the LAT average score was negative, while the CON average score was positive, and the difference between the two was significant (p ¼0.001). PC2 captured the amplitude variability in the interval 35–70% of the gait cycle (Fig. 4B). PC2 high curve was more extended with respect to the low curve and, consequently, a positive score respectively meant a more extended hip angle. LAT and CON had negative and positive average scores respectively and the difference between LAT and CON was significant (p o0.001). PC3 captured the temporal shift of the ANT group (Fig. 4C), which was significant not only versus CON (p ¼0.009), but also versus LAT (p¼0.011). Without entering into the same detailed explanation done for the hip sagittal angle PCs, the rest of the significant results are briefly summarized. PC1 of the hip frontal angle captured the significantly less adducted position of ANT with respect to CON (Fig. 4D). PC2 and PC3 for knee sagittal angle were also significantly different, and identified the decreased amplitude of LAT
4. Discussion As hypothesized, PCA identified magnitude and amplitude as main sources of variability in the frontal and sagittal angles and moments of the hip. Significant reductions occurred in THA patients compared to controls, confirming that the surgery does not completely restore normal gait, regardless of the adopted surgical approach (Bennett et al., 2009; Foucher et al., 2007; Lamontagne et al., 2011; Madsen et al., 2004; Martin et al., 2011; Meneghini et al., 2008; Queen et al., 2011). As expected, PCA was able to identify temporal differences from the CON group, such as the time delay in the patterns of ANT variables, not previously identified by traditional analyses. Statistical differences were also found in the sagittal plane variables of the neighboring joints, confirming all of our initial hypotheses. The significant reduction of the sagittal hip range of motion (PC1 and PC2) and of the sagittal knee angle (PC2 and PC3), and the significantly more dorsiflexed sagittal ankle angle (PC1) were interdependent because the ankle compensated for the reduced knee and hip ranges of motion with its increased dorsiflexion. Mont et al. (2007) found similar results and interpreted the more dorsiflexed ankle in the presence of reduced hip and knee ranges of motion as a shock absorption mechanism. Analogously, Tateuchi and colleagues (Tateuchi et al., 2011) correlated the reduced hip extension angle for unilateral THA patients with an increased dynamic hip joint stiffness, interpreting this as compensatory mechanism for the increased hip joint stiffness. Even though the aforementioned gait differences are dependent on each other, it is
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reasonable to assume that the alterations originated at the hip joint since this is the joint affected by the surgery. A possible explanation was found in the type of surgical approach.
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Indeed, the lateral THA involves the detachment of the anterior third of the gluteus medius, which is an important hip stabilizer, especially in the first 10% of the gait cycle (Lyons et al., 1983). Therefore, in cases where joint stability is not correctly restored, range of motion was reduced to act as a protection mechanism to minimize joint loadings (Lamontagne et al., 2009). Focusing on the ANT–CON comparison, the total score was mainly due to differences in magnitude and time shift of moment variables (Fig. 3). At the hip, the abduction angle magnitude was significantly reduced versus CON, and the abduction moment magnitude was significantly reduced versus both CON and LAT (PC1 for both variables, Table 2). This result was unexpected because the anterior approach should not have damaged the abductors, as opposed to the lateral approach. However, in the anterior approach, the space created to expose the femoral head requires the superficial splitting of the interval between the tensor fasciae latae and sartorius, two hip flexors and abductors. A cadaveric study by Meneghini et al. (2006) showed that the anterior approach could significantly damage these muscles during the surgery. Consequently, the reduced hip abduction moment, as well as the pattern delays (sagittal angles and moments identified by PC3s of hip, knee and ankle sagittal angles, Table 2) in the ANT group could be attributed to the damage sustained to the tensor fascia latae and sartorius. Even though from a physical point of view the tissue damage is restored at 300-day post-surgery, the time spent with an impaired muscle could lead to incorrect muscle adaptation and, consequently, to a change in muscular activity. Similarly, Madsen et al. (2004) and Meneghini et al. (2008) concluded that kinetic alterations found in THA patients at 6-week and 6-month follow-ups may be due to muscle injuries caused by the surgery. Therefore, our conclusions are coherent with previous findings, regardless of the follow-up time. However, to definitively confirm this concept an investigation on the hip muscle’s electromyographic activity would be warranted. The only two significant differences for LAT kinetic variables were in hip and knee sagittal moments (PC2 for both variables, Table 2), for which both the flexion and extension moments were reduced. This finding can be linked to the significantly reduced range of motion in the hip and knee sagittal angles (PC1 and PC2 for hip sagittal angle and PC2 for knee sagittal angle, Table 2); a less flexed or extended joint requires lower moments to counteract the action of its center of mass and, therefore, stabilize the joint. In conclusion, all our initial hypotheses were verified: PCA identified significant differences in gait for both THA groups compared to healthy controls, not only for the hip but also for the neighboring joints. The findings from the traditional discrete analysis (Lamontagne, et al., 2011) and from the PCA converged to the same conclusion that neither of the surgical techniques restores normal gait patterns. By conserving at least 90% of the available information, PCA captured differences in magnitude (significantly reduced sagittal and frontal moments in ANT) and amplitude (reduced sagittal angles in LAT), as previously found with traditional analysis approaches. PCA also identified temporal differences (significant pattern delays in ANT), that had not been noticed in previous THA studies. The direct comparison between
Fig. 4. These graphs report the PCs extracted from angular variables that were statistically significant. The PCs are represented as perturbation of the mean of the original signal, by adding (þ high) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The solid black, dark gray and light gray curves are the average curves for, respectively, the control (CON), lateral (LAT) and anterior (ANT) groups. The curves are reported for the entire gait cycle, from foot strike to foot strike. IFS ¼ipsilateral foot strike, CFO¼ controlateral foot off, CFS¼ controlateral footstrike, IFO¼ ipsilateral foot off.
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LAT and ANT was also statistically different for several variables and sources of variability. These results suggest that the highlighted alterations have different origins in the two groups: LAT mainly adopt the protective strategy of reducing the range of motion (amplitude) to decrease the hip load; ANT mainly changes motion pattern (magnitude and time delay) probably as consequence of an altered muscular strategy. The proposed point system was a useful tool for the interpretation and analysis of the results because it offered an immediate overview of the results, allowing for an easier way to create links between the significant variables. Nevertheless, the application of PCA had some limitations. Specifically, the PCs were not always easily interpretable and each source of variability had the same impact on the final score, even though it did not have the same functional and clinical importance. Therefore, the next challenge is to identify the PCs that are more related to gait functionality and consider different weights for the final score according to the variability content.
5. Conflict of interest statement None of the authors have any conflicts of interest to disclose.
Appendix A. Supplementary information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jbiomech.2012.05.041.
References
Fig. 5. These graphs report the PCs extracted from moment variables that were statistically significant. The PCs are represented as perturbation of the mean of the original signal, by adding (þ high) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The solid black, dark gray and light gray curves are the average curves for, respectively, the control (CON), lateral (LAT) and anterior (ANT) groups. The curves are reported for the stance phase, from foot strike to foot off. IFS¼ ipsilateral foot strike, CFO¼ controlateral foot off, CFS ¼controlateral footstrike, IFO ¼ipsilateral foot off.
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Mantovani, G., Lamontagne, M., Varin, D., Cerulli, G.G. Beaule´, P.E., 2011. Is principal component analysis more efficient to detect differences on biomechanical variables between groups? In: Proceedings of the 29th International Conference on Biomechanics in Sports, Porto, Portugal. Martin, R., Clayson, P.E., Troussel, S., Fraser, B.P., Docquier, P.L., 2011. Anterolateral minimally invasive total hip arthroplasty. A prospective randomized controlled study with a follow-up of 1 year. Journal of Arthroplasty 26, 1362–1372. Masonis, J.L., Bourne, R.B., 2002. Surgical approach, abductor function, and total hip arthroplasty dislocation. Clinical Orthopaedics and Related Research 405, 46–53. Matta, J.M., Shahrdar, C., Ferguson, T., 2005. Single-incision anterior approach for total hip arthroplasty on an orthopaedic table. Clinical Orthopaedics and Related Research 441, 115–124. McGinley, J.L., Baker, R., Wolfe, R., Morris, M.E., 2009. The reliability of threedimensional kinematic gait measurements: a systematic review. Gait and Posture 29, 360–369. Meneghini, R.M., Pagnano, M.W., Trousdale, R.T., Hozack, W.J., 2006. Muscle damage during MIS total hip arthroplasty: Smith–Petersen versus posterior approach. Clinical Orthopaedics and Related Research 453, 293–298. Meneghini, R.M., Smits, S.A., Swinford, R.R., Bahamonde, R.E., 2008. A Randomized, prospective study of 3 minimally invasive surgical approaches in total hip arthroplasty: comprehensive gait analysis. Journal of Arthroplasty 23, 68–73. Mont, M.A., Seyler, T.M., Ragland, P.S., Starr, R., Erhart, J., Bhave, A., 2007. Gait analysis of patients with resurfacing hip arthroplasty compared with hip osteoarthritis and standard total hip arthroplasty. Journal of Arthroplasty 22, 100–108.
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Contents lists available at SciVerse ScienceDirect
Journal of Biomechanics journal homepage: www.elsevier.com/locate/jbiomech www.JBiomech.com
Comparison of total hip arthroplasty surgical approaches by Principal Component Analysis Giulia Mantovani a, Mario Lamontagne a,n, Daniel Varin a, Giuliano G. Cerulli b, Paul E. Beaule´ c a
School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada Let People Move Research Institute, Perugia/Arezzo, Italy c Division of Orthopedic Surgery, University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada b
a r t i c l e i n f o
abstract
Article history: Accepted 24 May 2012
Gait adaptations are persistent after total hip arthroplasty and can depend on the type of surgery. This study focused on two surgical approaches: anterior and lateral. To analyze gait adaptations, biomechanical analyses usually employ an a priori selection of the parameters that leads to incomplete or redundant information. In contrast, Principal Component Analysis (PCA) provides an efficient transformation of the dataset by automatically identifying the major sources of variability. The purpose of this study was to investigate differences in level-walking among three groups of participants using PCA: patients undergoing an anterior surgical approach, patients undergoing a lateral surgical approach, and healthy controls. Biomechanical descriptions of the extracted principal components aided in the interpretation of the statistically significant results obtained from multivariate analysis of covariance (MANCOVA) tests. A point system was introduced to summarize the results and guide the interpretation. PCA captured reduced magnitude in sagittal and frontal moments in the anterior approach group, and reduced sagittal peaks angle in the lateral group, as previously found with traditional analyses. PCA also identified significant pattern delays in the anterior group, unnoticed in previous studies. In conclusion, neither surgical approach restored normal gait functionality because lower extremity kinetics and kinematics alterations persisted at 300-day follow-up after the surgery, regardless of the technique. & 2012 Elsevier Ltd. All rights reserved.
Keywords: Principal Component Analysis Total hip arthroscopy Variability Lateral surgical approach Anterior surgical approach
1. Introduction Despite significant improvements in gait patterns, gait adaptations persist after total hip arthroplasty (THA) in both short and long term follow-up (Bennett et al., 2009; Foucher et al., 2007). The choice of surgery can influence postoperative gait; with this in mind, this study compares two different surgical approaches: the modified direct lateral approach (Mulliken et al., 1998) and the single-incision anterior approach (Matta et al., 2005). The lateral approach involves the detachment of the gluteus medius and minimus from the greater trochanter, leading to abductor dysfunction and a postoperative limp (Masonis and Bourne, 2002). Conversely, the anterior approach spares the stabilizer structures of the pelvis (Matta et al., 2005), with a reduced risk of dislocation (Nakata et al., 2009; Sariali et al., 2008). Biomechanical investigations have slightly favored the anterior approach in short-term follow-up (Klausmeier et al., 2010; Restrepo et al., 2010) but show the persistence of abnormalities in long-term follow-up,
n
Corresponding author. Tel.: þ1 613 562 5800x4258; fax: þ1 613 562 5328. E-mail address: [email protected] (M. Lamontagne).
0021-9290/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jbiomech.2012.05.041
regardless of the type of surgery (gait analysis studies: Beaulieu et al., 2010; Madsen et al., 2004; Martin et al., 2011; Meneghini et al., 2008; Queen et al., 2011; and stairs climbing analysis: Lamontagne et al., 2011). In particular, at a 16-week follow-up, Klausmeier et al. (2010) found a significant reduction in the frontal and sagittal plane hip range of motion and in the frontal plane peak moment in both anterior and anterolateral approaches respect to controls. No secondary effects on the other joints were analyzed even though it has been shown that neighboring joints are also affected by primary alterations on the hip (Perron et al., 2000). In biomechanical evaluations, parameters extracted from data usually include peaks and ranges of variation of the kinetics and kinematics variables (Lamontagne et al., 2009). These parameters are selected a priori, potentially leading to incomplete or redundant information (Mantovani et al., 2011). Principal Component Analysis (PCA) is adopted to derive an efficient representation of the original dataset by retaining potentially valuable temporal information (Chau, 2001), and overcoming the problem of the a priori parameters selection (O’Connor and Bottum, 2009). PCA can detect significant differences among groups of participants, and the differences can be correlated to specific pathological
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conditions by introducing an interpretation of the newly transformed waveforms (Deluzio and Astephen, 2007; Dona et al., 2009; Landry et al., 2007; Wrigley et al., 2006). Thus, our purpose was to analyze the kinetics and kinematics of the lower-extremity joints during walking: the study compared healthy controls and THA patients operated with an anterior or lateral surgical approach, at an average of te10-months follow-up, by using PCA. A point system based on PCA outcomes was also introduced to simplify the interpretation of results. Since previous traditional analyses identified differences mainly in the frontal and sagittal hip ranges of motion and peak moments (Klausmeier et al., 2010; Lamontagne et al., 2011), we hypothesized that PCA would identify magnitude and amplitude as main sources of variability in the same variables (Deluzio and Astephen, 2007; Landry et al., 2007). It was also hypothesized that magnitude and amplitude of frontal and sagittal hip angles and moments would be reduced in THA patients compared to the controls. We also expected to show differences not detected with previous a priori parameter selection, such as differences in temporal and shape characteristics (Deluzio and Astephen, 2007; Landry et al., 2007; Wrigley et al., 2006). Lastly, significant secondary effects in the neighboring joints directly linked to the primary alterations of the hip variables were expected (Perron et al., 2000).
2. Methods 2.1. Subjects and protocol Sixty participants were recruited and divided into three groups matched for age and body mass index (Table 1). Twenty patients had THA by means of anterior approach (ANT), 20 by lateral approach (LAT) and 20 were healthy controls (CON). All patients were recruited approximately 300 days after their surgery (Table 1), had undergone primary unilateral THA for reasons other than infection or fracture, and were not suffering from any other known condition that could alter their gait. The type of surgical approach was not randomized; however, the patients were not selected prior to surgery, hence there was no bias concerning the choice of the surgery. Three different physicians performed the surgeries and two different implants were used (Table 1). The institution’s research ethics board approved the study and the patients provided written informed consent. Participants performed three level-walking trials at a self-selected pace, which was preferred over imposing a pre-defined walking speed, to create experimental conditions as close as possible to everyday situations encountered by the patients. The statistical analysis revealed that walking speed was not significantly different among the three groups. Three-dimensional trajectories of a modified Helen Hayes marker-set (Lamontagne et al., 2011), and ground reaction forces were collected with a 9-camera motion analysis system (Vicon MX-13) and two Kistler force plates. Through inverse kinematics and kinetics models, joint angles and moments were calculated, and moments were normalized by body weight (Lamontagne et al., 2011). The transverse plane variables were not included in the analysis because of the high inter-subject variability reported for this plane (McGinley et al., 2009). In summary, a total of 12 waveforms were used for the PCA: the
sagittal and frontal angles and moments for the hip, knee and ankle joints. The waveforms were time-normalized to the gait cycle percentage using a cubic spline function. The region of interest for angles was from foot-strike to foot-strike of the affected side, while for moments was from foot-strike to toe-off of the affected side. Three trials for each subject were averaged.
2.2. PCA: background and application to gait analysis PCA consists of an orthogonal transformation that converts a set of observations of m correlated variables X ¼ ½x1 ,x2 ,. . .,xm T into a set of uncorrelated ones (i.e., principal components or PCs), Y ¼ ½y1 ,y2 ,. . .,yd T , with d o m. The transformation is defined by the equation Y ¼ AT X, where the columns of the matrix Amd ¼ ½A1 ,A2 ,. . .,Ad are the first d eigenvectors (sorted from the largest eigenvalue, li) of the covariance matrix of X. In doing this, the new dataset Y contains the majority of the information of the original dataset, which can be represented by only d components instead of m. Y values are called scores, and the columns of A are the loadings (Jolliffe, 2002). In gait analysis, PCA can be employed to identify the dominant sources of variability among subjects with respect to a specific variable (O’Connor and Bottum, 2009). In this study, a PCA was performed for each variable: the correlated variables were the time points m ¼ 101 with p observations corresponding to the number of participants (in this study p ¼ 60). The transformation equation was Y ¼ AT ðXX Þ, where X 101x60 was the matrix of horizontally concatenated waveforms and X 101x60 ¼ ½x 1 ,x 2 ,. . .,x 101 T was the matrix of the inter-participant averages (Dona et al., 2009). The means were subtracted to make PCA capture the variability among subjects and not the absolute values. The matrix Y dx60 contained the scores, and high scores participants signified high correlation with the specific source of variability identified by that PC. d was the number of retained PCs and was defined based on the cumulative variability criterion (Jolliffe, 2002). The variability content of a PC was defined as the percentage P vari % ¼ li = m j ¼ 1 lj , and the cumulative variability was given by cumvar i % ¼ Pm Pi j ¼ 1 lj = j ¼ 1 lj . The criterion suggested to stop when cumvar i % was higher than a certain threshold, which we set at 90%. This threshold was also used by Lee et al. (2009) and it can be considered a good compromise between the necessity of retaining enough information content and the interpretability of the PCs that becomes more difficult at higher PC numbers. To help the interpretation of the PCs, they were represented as suggested by Ramsay and Silverman (2002), who portrayed the effect of a PC about the mean curve of the original signal by adding (þ) and subtracting ( ) a multiple Q of the PC loadings to the mean curve itself. Depending on the behavior of mean waveforms, they suggested the possibility of subjectively adjusting the Q value. Therefore, we used the 5th and 95th percentiles of the PC score distributions as Q values for the low ( ) and high (þ ) curves respectively (Figs. 1 and 2), because the single PC contribution to the total waveform was more clearly represented.
2.3. Statistics For each of the 12 original variables, the main effect of the surgical approach (independent variable) was analyzed in a one-way multivariate analysis of covariance (MANCOVA), which used PCs scores as dependent variables. Using gender as covariate, possible interactions between surgical approach and the unbalanced female/male ratio among groups was controlled. Post-hoc analyses verified the specific pairwise comparisons (LAT-CON, ANT-CON, LAT-ANT), and Bonferroni’s correction adjusted the significance level to a ¼ 0.017, preventing for type I errors. The homogeneity-of-regression assumption was first tested to
Table 1 The columns of the table report: group name, gender ratio (number of female/number of male), age, body mass index (BMI), time of testing after surgery, walking speed (all expressed in terms of mean and standard deviations in round brackets), list of surgeons who performed the operations (initials and number of operated subjects in round brackets) and type of prosthetic implants (name of prosthesis and number of subjects who got it). Group
Female/male
Age (years)
BMI (kg/m2)
Time after THA (days)
Walking speed (m/s)a
CON LAT
10/10 10/10
63.5(4.4) 66.2(6.7)
24.9(3.5) 27.2(5.0)
323(79)
1.29(0.15) 1.14(0.21)
Surgeons
Type of implant (N)
PB (9)
Stryker Accolade stem with Trident acetabulum (13)
PK (6) RF (5) ANT
14/6
60.5(6.0)
28.5(4.9)
291(114)
1.31(0.15)
PB (17) PK (3)
a
The result from the ANOVA test on the walking speed is not statistically significant (p ¼0.051).
Wright Medical Profemur stem with Conserve Plus acetabulum (7) Stryker Accolade stem with Trident acetabulum (5) Wright Medical Profemur stem with either Lineage (8), Conserve Plus (6) or Dynasty (1) acetabulum
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Fig. 1. The average hip sagittal angle of the complete dataset is plotted (black solid curve) together with the PC2 (A) and PC3 (B) effects, respectively amplitude and time shift. The variability explained by the PCs is also reported as percentage. The PCs are represented as perturbation of the mean of the original signal, by adding (þhigh) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The scatter plot (C) reports the scores distribution relative to the PCs of interest, where every participant is represented by a marker. AMP¼ amplitude, TS¼time shift, CON¼ control, LAT¼ lateral, ANT¼ anterior. exclude any significant interaction between gender and surgical approach. Preliminary assumption testing was conducted to check for normality, linearity, outliers, and homogeneity of variance–covariance and multicollinearity, with no severe violations noted. Walking speed differences among the groups were tested by an ANOVA (a ¼ 0.05) to exclude possible influences on the kinematics and kinetics analyses, and no significant differences were found (Table 1). A point system was created based on a similar approach proposed by Deluzio et al. (1997) to summarize the statistical results: each statistically significant difference from the pairwise comparisons was worth one point. If no significant difference was found, no points were added. Notably, a low score for a group indicated a higher similarity to the CON group. The differences in the point system score do not necessarily provide a complete understanding of the results but are only used as reference for clinical interpretation. The calculations for the PCA were done using Matlabs (version R2010a, The MathWorks, Natik, MA, USA), while the statistical analysis was carried out with SPSS version 19 (SPSS Inc., Chicago, IL, USA).
2.4. Biomechanical interpretation of the principal components PCs were divided into four main sources of variability that could be interpreted as biomechanical parameters: amplitude (AMP), magnitude (MAG), time shift (TS) and pattern (PAT). AMP identified a variation in the peaks’ amplitude of the waveforms (Fig. 1A). The presence of MAG indicated a large variability in the averages of the original dataset (i.e., an offset among the original trials), which produced a vertical shift of the curves (Fig. 2A). TS captured a change in the timing of the waveform events, which produced a horizontal shift (Fig. 1B). PAT identified specific changes in the waveform patterns that do not fall into the other categories (Fig. 2B). The same PC can be well interpreted as more than one source of variability at the time (for example, AMP and TS). In the proposed point system, the points gained by LAT and ANT were sorted according to sources of variability and type of original variable (angle and moment), linking statistical results and functional meanings of the variables. In case one PC was interpreted with two sources of variability and was statistically
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Fig. 2. The average hip frontal moment of the complete dataset is plotted (black solid curve) together with the PC1 (A) and PC2 (B) effects, respectively magnitude and pattern. The variability explained by the PCs is also reported as percentage. The PCs are represented as perturbation of the mean of the original signal, by adding (þ high) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The scatter plot (C) reports the scores distribution relative to the PCs of interest, where every participant is represented by a marker. MAG¼ magnitude, PAT¼ pattern, CON¼ control, LAT¼ lateral, ANT¼anterior. significant, the point system would only add half a point to conserve the total score.
3. Results Beside PC1 and PC3 of frontal knee moment, which were not considered for the rest of the analysis, the homogeneity-ofregression assumption was not significant for any variable (Table s1). Therefore, there were no significant interactions between the covariate (gender) and the independent variable (group), which justifies the use of the MANCOVA test. PCs’ variability content, post-hoc analysis p-values and sources of variability labels are reported only when there was statistical significance (Table 2). A complete table that reports scores’ averages and standard deviations, and the non-significant results can be found in the supplementary material (Table s1). The statistical results were summarized in the point system (Fig. 3); TOT (total) is the sum of the scores from different sources of variability and showed that THA patients maintained significant differences in kinematic and kinetic variables, regardless of the surgical approach. The two approaches did not always result in similar gait alterations; differences were observed between the two surgical groups, particularly in the kinetic variables. For LAT, the majority of the significant differences concerned peak amplitude, while the differences for ANT were mainly given by magnitude and time shift.
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Table 2 The table reports the results of the post-hoc analyses following the MANCOVA tests on the PC scores. The ‘Source of variability’ column reports the labels describing the identified source of variability of the PCs: MAG (magnitude), AMP (amplitude), TS (time shift) and PAT (pattern). The post-hoc analysis results indicate the statistical different pairwise comparisons. Variables
PC
Sources of variability
Post-hoc Analysisa LAT–CON
Hip angle (sagittal)
Hip angle (Frontal) Knee angle (sagittal) Ankle angle (sagittal) Hip moment (sagittal) Hip moment (frontal) Knee moment (sagittal) Ankle moment (sagittal)
PC1 PC2 PC3 PC1 PC2 PC3 PC1 PC2 PC1 PC1 PC2 PC1 PC3
AMP AMP TS MAG AMP þTS AMP þTS MAG AMP MAG MAG AMP MAG þTS TS
ANT–CON
0.001 (LAT o CON) o0.001 (LATo CON) 0.009 (ANT 4CON) 0.004 (ANT o CON) 0.001 (LATo CON) o0.001 (LAT4CON) 0.001 (LAT 4CON) 0.013 (LAT 4CON)
LAT–ANT
0.002 (LATo ANT) 0.011 (LATo ANT) o0.001 (LAToANT)
o0.001 (ANT 4CON)
o0.001 (ANT 4CON) o0.001 (ANT oCON) o0.001 (LAT4CON) o0.001 (ANT oCON) 0.008 (ANT 4CON)
0.010 (LAT4ANT) o0.001 (LAToANT) o0.001 (LAT4ANT) 0.007 (LAT4ANT) o0.001 (LAT4ANT) 0.005 (LATo ANT)
Direction of the difference between the mean scores is reported in brackets. a
statistically significant, Bonferroni’s correction a ¼0.017.
and ANT and the slightly delayed pattern for ANT (Fig. 4E and F). PC1 of the sagittal ankle angle captured the more dorsiflexed LAT curve (Fig. 4G), which was significantly different from CON. PC2 of hip sagittal moment captured the decreased amplitude of the peaks in LAT (Fig. 5A). PC1 of the hip frontal moment captured the global magnitude of the waveforms, with ANT showing a significantly less adducted moment versus CON and LAT (Fig. 5B and C). PC1 and PC2 of the sagittal knee moment captured the difference in magnitude and amplitude among the groups (Fig. 5C and D), reporting a significantly more extended curve for ANT and lower peaks for LAT. Lastly, PC1 of the sagittal flexion moment of the ankle joint captured the higher magnitude of the ANT group (Fig. 5E), statistically different from LAT and CON, while PC3 identified the time shift in the ANT curve (Fig. 5F). Fig. 3. The graphs summarize the results from Table 2 according to the point system that assigns 1 point to each statistically significant difference and 0.5 point if two sources of variability are identified for the same PC. The results are sorted according to the type of original variable and source of variability. The higher the column, the less similar are the lateral or anterior groups to the control group. MAG¼ magnitude, AMP¼ amplitude, TS ¼ time shift, PAT ¼pattern, CON¼ control, LAT¼ lateral, ANT ¼anterior.
At the hip sagittal angle, PC1 captured the reduced amplitude of LAT (Fig. 4A), in the intervals 0–35% and 70–100%, with low curve being less flexed than high curve. Consequently, a participant with a negative score for this PC had a less flexed hip angle with respect to the controls. In this interval, LAT was closer to the low curve and CON to the high curve and, in fact, the LAT average score was negative, while the CON average score was positive, and the difference between the two was significant (p ¼0.001). PC2 captured the amplitude variability in the interval 35–70% of the gait cycle (Fig. 4B). PC2 high curve was more extended with respect to the low curve and, consequently, a positive score respectively meant a more extended hip angle. LAT and CON had negative and positive average scores respectively and the difference between LAT and CON was significant (p o0.001). PC3 captured the temporal shift of the ANT group (Fig. 4C), which was significant not only versus CON (p ¼0.009), but also versus LAT (p¼0.011). Without entering into the same detailed explanation done for the hip sagittal angle PCs, the rest of the significant results are briefly summarized. PC1 of the hip frontal angle captured the significantly less adducted position of ANT with respect to CON (Fig. 4D). PC2 and PC3 for knee sagittal angle were also significantly different, and identified the decreased amplitude of LAT
4. Discussion As hypothesized, PCA identified magnitude and amplitude as main sources of variability in the frontal and sagittal angles and moments of the hip. Significant reductions occurred in THA patients compared to controls, confirming that the surgery does not completely restore normal gait, regardless of the adopted surgical approach (Bennett et al., 2009; Foucher et al., 2007; Lamontagne et al., 2011; Madsen et al., 2004; Martin et al., 2011; Meneghini et al., 2008; Queen et al., 2011). As expected, PCA was able to identify temporal differences from the CON group, such as the time delay in the patterns of ANT variables, not previously identified by traditional analyses. Statistical differences were also found in the sagittal plane variables of the neighboring joints, confirming all of our initial hypotheses. The significant reduction of the sagittal hip range of motion (PC1 and PC2) and of the sagittal knee angle (PC2 and PC3), and the significantly more dorsiflexed sagittal ankle angle (PC1) were interdependent because the ankle compensated for the reduced knee and hip ranges of motion with its increased dorsiflexion. Mont et al. (2007) found similar results and interpreted the more dorsiflexed ankle in the presence of reduced hip and knee ranges of motion as a shock absorption mechanism. Analogously, Tateuchi and colleagues (Tateuchi et al., 2011) correlated the reduced hip extension angle for unilateral THA patients with an increased dynamic hip joint stiffness, interpreting this as compensatory mechanism for the increased hip joint stiffness. Even though the aforementioned gait differences are dependent on each other, it is
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reasonable to assume that the alterations originated at the hip joint since this is the joint affected by the surgery. A possible explanation was found in the type of surgical approach.
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Indeed, the lateral THA involves the detachment of the anterior third of the gluteus medius, which is an important hip stabilizer, especially in the first 10% of the gait cycle (Lyons et al., 1983). Therefore, in cases where joint stability is not correctly restored, range of motion was reduced to act as a protection mechanism to minimize joint loadings (Lamontagne et al., 2009). Focusing on the ANT–CON comparison, the total score was mainly due to differences in magnitude and time shift of moment variables (Fig. 3). At the hip, the abduction angle magnitude was significantly reduced versus CON, and the abduction moment magnitude was significantly reduced versus both CON and LAT (PC1 for both variables, Table 2). This result was unexpected because the anterior approach should not have damaged the abductors, as opposed to the lateral approach. However, in the anterior approach, the space created to expose the femoral head requires the superficial splitting of the interval between the tensor fasciae latae and sartorius, two hip flexors and abductors. A cadaveric study by Meneghini et al. (2006) showed that the anterior approach could significantly damage these muscles during the surgery. Consequently, the reduced hip abduction moment, as well as the pattern delays (sagittal angles and moments identified by PC3s of hip, knee and ankle sagittal angles, Table 2) in the ANT group could be attributed to the damage sustained to the tensor fascia latae and sartorius. Even though from a physical point of view the tissue damage is restored at 300-day post-surgery, the time spent with an impaired muscle could lead to incorrect muscle adaptation and, consequently, to a change in muscular activity. Similarly, Madsen et al. (2004) and Meneghini et al. (2008) concluded that kinetic alterations found in THA patients at 6-week and 6-month follow-ups may be due to muscle injuries caused by the surgery. Therefore, our conclusions are coherent with previous findings, regardless of the follow-up time. However, to definitively confirm this concept an investigation on the hip muscle’s electromyographic activity would be warranted. The only two significant differences for LAT kinetic variables were in hip and knee sagittal moments (PC2 for both variables, Table 2), for which both the flexion and extension moments were reduced. This finding can be linked to the significantly reduced range of motion in the hip and knee sagittal angles (PC1 and PC2 for hip sagittal angle and PC2 for knee sagittal angle, Table 2); a less flexed or extended joint requires lower moments to counteract the action of its center of mass and, therefore, stabilize the joint. In conclusion, all our initial hypotheses were verified: PCA identified significant differences in gait for both THA groups compared to healthy controls, not only for the hip but also for the neighboring joints. The findings from the traditional discrete analysis (Lamontagne, et al., 2011) and from the PCA converged to the same conclusion that neither of the surgical techniques restores normal gait patterns. By conserving at least 90% of the available information, PCA captured differences in magnitude (significantly reduced sagittal and frontal moments in ANT) and amplitude (reduced sagittal angles in LAT), as previously found with traditional analysis approaches. PCA also identified temporal differences (significant pattern delays in ANT), that had not been noticed in previous THA studies. The direct comparison between
Fig. 4. These graphs report the PCs extracted from angular variables that were statistically significant. The PCs are represented as perturbation of the mean of the original signal, by adding (þ high) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The solid black, dark gray and light gray curves are the average curves for, respectively, the control (CON), lateral (LAT) and anterior (ANT) groups. The curves are reported for the entire gait cycle, from foot strike to foot strike. IFS ¼ipsilateral foot strike, CFO¼ controlateral foot off, CFS¼ controlateral footstrike, IFO¼ ipsilateral foot off.
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LAT and ANT was also statistically different for several variables and sources of variability. These results suggest that the highlighted alterations have different origins in the two groups: LAT mainly adopt the protective strategy of reducing the range of motion (amplitude) to decrease the hip load; ANT mainly changes motion pattern (magnitude and time delay) probably as consequence of an altered muscular strategy. The proposed point system was a useful tool for the interpretation and analysis of the results because it offered an immediate overview of the results, allowing for an easier way to create links between the significant variables. Nevertheless, the application of PCA had some limitations. Specifically, the PCs were not always easily interpretable and each source of variability had the same impact on the final score, even though it did not have the same functional and clinical importance. Therefore, the next challenge is to identify the PCs that are more related to gait functionality and consider different weights for the final score according to the variability content.
5. Conflict of interest statement None of the authors have any conflicts of interest to disclose.
Appendix A. Supplementary information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jbiomech.2012.05.041.
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Fig. 5. These graphs report the PCs extracted from moment variables that were statistically significant. The PCs are represented as perturbation of the mean of the original signal, by adding (þ high) and subtracting ( low) a multiple Q of the PC loading. Q is respectively the 95th and 5th percentile of the score distribution. The solid black, dark gray and light gray curves are the average curves for, respectively, the control (CON), lateral (LAT) and anterior (ANT) groups. The curves are reported for the stance phase, from foot strike to foot off. IFS¼ ipsilateral foot strike, CFO¼ controlateral foot off, CFS ¼controlateral footstrike, IFO ¼ipsilateral foot off.
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