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P059 Can gait patterns in children with hemiplegic cerebral palsy be clustered into distinct clinically relevant groups?
Abstracts of the 17th Annual Meeting of ESMAC, Poster Presentations / Gait & Posture 28S (2008) S49–S118 using multi-level random effects linear regression with maximumlikelihood estimation [1,2]. Variability in data can be attributed to between-assessors, within-assessor and inter-trial. GRPs will also illustrate data variation against recommended error targets. Results: Detailed variance components results are presented in pdf reports for the team and for individual assessors. Also produced are colour coded samples of raw data, and an interactive web-based presentation of results through Gaitabase. Examples of GRP from an individual assessor (top) and a team of assessors (bottom) are illustrated in Figure 1. We recommend a reliability target level of a standard deviation, SD, of less than five degrees, based upon a review of the literature and our clinical experience. The individual assessor and team of assessors in the example below both meet this target for all gait variables. Discussion: The gait analysis community recognises that data quality is an important concern. The proposed scheme enables laboratories and individuals to provide standardised evidence of the reliability of their data. References
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10-point scale. Information on gait patterns were provided on CD-ROM and contained kinematics in the sagittal, coronal and transverse planes and a video clip of walking from the sagittal and coronal views. Similarity scores from clinicians for the possible 1128 unique pair-wise comparisons of gait patterns were used to produce a ‘Distance’ matrix of proximity data. Non-metric MDS using ALSCAL was used to determine the structure of the proximity data. Results: Inspection of the MDS Scree plot revealed that either a three or four-dimensional solution provided the best representation of the data. The MDS configuration map displaying the fitted distances of each gait pattern in Euclidean distance revealed no distinct clustering patterns. The configuration was re-modelled into Polygon software (VICON, Oxford UK) (Figure 1) to enable a more clear inspection of the configuration map represented in three or more dimensions. Inspection of Figure 1, including when viewed from multiple angles, again revealed no distinct clusters of data points that were close to each other and were separated clearly from other clustered points.
[1] Cox D, Solomon P. Components of Variance: CRC Press; 2003. [2] Stata Statistical Software: Release 9. 2005, StataCorp LP: College Station, TX.
P059 Can gait patterns in children with hemiplegic cerebral palsy be clustered into distinct clinically relevant groups? F. Dobson1 , H.K. Graham2 , M. Morris3 . 1 Hugh Williamson Gait Laboratory, Murdoch Research Institute; 2 Department of Orthopaedic Surgery, Royal Children’s Hospital; 3 School of Physiotherapy, The University of Melbourne, Australia Summary: This study explored the ability to detect clinically relevant groups of gait patterns from a population-based cohort of children with hemiplegic CP (hCP). Multi-dimensional scaling (MDS) was used to establish the structure of the gait pattern data and investigate whether it was best represented as distinct group entities or by an underlying continuum of characteristics. Exploration of the structure of gait pattern data was based on expert clinical judgments using pattern recognition techniques. Conclusions: No clear clusters of gait patterns were found in the MDS configuration. Rather, the gait data structure was best represented by an underlying continuum of data points based on at least three independent dimensions. Introduction: The diversity of gait deviations observed in children with CP has lead to repeated efforts to develop gait classification systems to assist in diagnosis, clinical-decision making and communication [1]. For these tools to be useful, they need to be able to differentiate gait patterns into distinct meaningful clinical categories [1]. Many attempts to classify gait previously have relied on methods that either arbitrarily or statistically allocate gait patterns into groups that may hold little clinical relevance [2]. Exploration of underlying structure of gait data and whether it can suitable by allocated into distinct clusters has received very little attention in previous literature of gait pattern classification. Patients/Materials and Methods: An expert panel of 24 clinicians, with an average of 10 years experience in instrumented gait analysis in children with CP, were invited to make judgements on the similarity of 48 different pairs of gait patterns using a
Figure 1. Configuration of the data on the first three dimensions. Discussion: The lack of clear clusters suggests that gait patterns from children with hCP appeared to lie more on a continuum of points rather than in distinct groups. These results suggest that gait classification for children with hemiplegia are more likely imposed on an underlying continuum of data structure rather that representing real distinct groups. This observation was however based on similarity scores from expert clinical judgments rather than absolute differences in gait curves. Further validation of this finding using quantifiable differences in gait curves is warranted. The structure of data identified in this investigation revealed that the kinematic gait data could be sufficiently represented by three to four dimensions allowing the complex nature of the data to be represented by a reduced number of dimensions that still adequately capture the variation in the data. This suggests that different approaches to describing gait patterns may be more appropriate and that the application of a score rather than group may better represent the continuum of data over the three to four independent dimensions. Further work is required to explore such dimensional scores.
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Abstracts of the 17th Annual Meeting of ESMAC, Poster Presentations / Gait & Posture 28S (2008) S49–S118
References [1] Dobson F, Morris ME, Baker R, Graham HK. Gait Posture 2006; 25: 140–152. [2] Chau T. Gait Posture 2001; 13: 46−66.
P060 Hemiplegic cerebral palsy: gait patterns and motor function across a population based cohort F. Dobson1 , H.K. Graham2 , R. Baker1 , M. Morris3 . 1 Hugh Williamson Gait Analysis Laboratory, Murdoch Children’s Research Institute; 2 Department of Orthopaedic Surgery, Royal Children’s Hospital; 3 School of Physiotherapy, The University of Melbourne, Australia Summary: This study examined the relationships between gait patterns and motor function classifications in a population-based population cohort of children with hemiplegic cerebral palsy (hCP) and compared the distribution of gait patterns in the current cohort to the original Winters Gage and Hicks (WGH) referralbased cohort. Conclusions: This first ever population based study of gait patterns in hCP achieved a recruitment rate of 71%. All but two of the 69 children could be categorised with the WGH classification. None were identified as WGH Group III. GMFCS level was associated with WGH type but there were only weak associations between categorisations of upper and lower limb function. Introduction: Even within well-defined subgroups of CP, such as spastic hemiplegia (hCP), there is marked variability in the presentation and function between individuals [1]. In response, several methods for differentiating individuals into more homogeneous subgroups have been developed. Previous attempts to classify gait patterns in CP predominately utilized cohorts that were referred to a service and as such, these classifications may not be representative of the total variation possible in the subtype(s) of the CP population. Patients/Materials and Methods: An attempt was made to include all eligible children identified with hCP on the Victorian Cerebral Palsy Register born in Victoria, Australia between the years 1990–1992. All children underwent standardised physical examination and 3-D gait analysis. Population characteristics were reported using the WGH classification for gait patterns [2]; the Manual Activity Classification System (MACS) [3] and House classification [4] for bilateral and unilateral upper limb function; and the Gross Motor Function Classification System (GMFCS) and Functional Mobility Scale (FMS) [5] for gross motor function. Results: A recruitment rate of 71% (69/97) was achieved. The majority of children (33%) demonstrated a WGH Group I gait pattern, 27% a Group II pattern, and 20% a Group IV pattern. Two gait patterns were non-classifiable using WGH descriptions. No gait patterns represented WGH Group III. The proportions of gait pattern groups between the current and original WGH cohorts were not significantly different [c2(d.f. = 2) = 1.47, p = 0.48]. All children were classified as GMFCS level I or II. Group I gait patterns were mostly classified as GMFCS level I (96%) whereas Group IV gait patterns were mostly classified as GMFCS level II (71%). A ceiling effect was observed on the FMS for the 5 and 50-metre distances, however there was greater variability of scores over the 500-metre distance. All children were classified as either MACS level 1, 2 or 3. Children with gait pattern groups I and II were mostly classified as MACS level 1. Children with Group
IV gait patterns were mostly classified as MACS level 2 or 3. Some children demonstrated mild involvement of the lower limb (i.e. WGH Group I) with more severe involvement of the upper limb (MACS level 3). All grades on the House classification were represented. There was only a weak association between lower limb involvement and upper limb involvement using the MACS (R = 0.49, p = 0.0001) and the House classification (R = 0.51, p < 0.0001). Discussion: This study confirmed that hCP generates a wide spectrum of variations. Many children with hCP demonstrated very high level of functioning and relatively low level of impairment. Most of these children were managed adequately in the community where access to physiotherapy, rehabilitation and orthotic management was appropriate and cost effective. A smaller number of individuals required more complex management where instrumented gait analysis was appropriate and beneficial to clinical decision making. The weak association between lower and upper limb involvement is consistent with the findings from previous studies [1] and supports the need for separate classification systems of upper limb and lower limb functioning in this group of children. Aside from the absence of WGH Group III gait patterns, the proportions and distributions of gait patterns were comparable between the current cohort and the original WGH cohort supporting the WGH system as a foundation for the development of any new or modified gait classifications in children with hCP. References [1] Damiano D, Abel M, Romness M, Oeffinger D, et al. (2006). Dev Med Child Neurol, 48(10), 797–803. [2] Winters F, Gage JR, Hicks R. J Bone Joint Surg 1987: 69-A(3): 437– 441. [3] Eliasson A-C, Krumlinde-Sundholm L, Rosblad B, et al. Dev Med Child Neurol 2006; 48: 549−54. [4] House JH, Gwathmey FW, Fidler MO. J Bone Joint Surg 1981; 63A: 216–225. [5] Graham HK, Harvey A, Rodda J, et al. J Pediatr Orthop 2004; 24: 514–520.
P061 Evaluating the outcome of single event multilevel surgery: find the way use the MAP (movement analysis profile) P. Thomason1 , X. Yu1 , R. Baker1 , H.K. Graham1 . 1 Gait CCRE Murdoch Children’s Research Institute, Royal Children’s Hospital, Australia Summary: The Movement Analysis Profile (MAP) is used to evaluate outcome following Single Event Multilevel Surgery (SEMLS) in a consecutive sample of 18 children with bilateral cerebral palsy (CP). Conclusions: The MAP illustrates overall improvement in gait following SEMLS and in individual kinematic parameters. Introduction: Several studies have found improvement after SEMLS using selected kinematic parameters [1−4] but only one [5] has done so using an overall index of gait pathology (the Gillette Gait Index, GGI). The aims of this study were to use the newly developed MAP to evaluate the outcome of SEMLS and the contribution of individual surgical procedures on outcome at 12 months post surgery. Patients/Materials and Methods: A consecutive sample of 18 children (11 male) with bilateral spastic CP, GMFCS Levels II
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10-point scale. Information on gait patterns were provided on CD-ROM and contained kinematics in the sagittal, coronal and transverse planes and a video clip of walking from the sagittal and coronal views. Similarity scores from clinicians for the possible 1128 unique pair-wise comparisons of gait patterns were used to produce a ‘Distance’ matrix of proximity data. Non-metric MDS using ALSCAL was used to determine the structure of the proximity data. Results: Inspection of the MDS Scree plot revealed that either a three or four-dimensional solution provided the best representation of the data. The MDS configuration map displaying the fitted distances of each gait pattern in Euclidean distance revealed no distinct clustering patterns. The configuration was re-modelled into Polygon software (VICON, Oxford UK) (Figure 1) to enable a more clear inspection of the configuration map represented in three or more dimensions. Inspection of Figure 1, including when viewed from multiple angles, again revealed no distinct clusters of data points that were close to each other and were separated clearly from other clustered points.
[1] Cox D, Solomon P. Components of Variance: CRC Press; 2003. [2] Stata Statistical Software: Release 9. 2005, StataCorp LP: College Station, TX.
P059 Can gait patterns in children with hemiplegic cerebral palsy be clustered into distinct clinically relevant groups? F. Dobson1 , H.K. Graham2 , M. Morris3 . 1 Hugh Williamson Gait Laboratory, Murdoch Research Institute; 2 Department of Orthopaedic Surgery, Royal Children’s Hospital; 3 School of Physiotherapy, The University of Melbourne, Australia Summary: This study explored the ability to detect clinically relevant groups of gait patterns from a population-based cohort of children with hemiplegic CP (hCP). Multi-dimensional scaling (MDS) was used to establish the structure of the gait pattern data and investigate whether it was best represented as distinct group entities or by an underlying continuum of characteristics. Exploration of the structure of gait pattern data was based on expert clinical judgments using pattern recognition techniques. Conclusions: No clear clusters of gait patterns were found in the MDS configuration. Rather, the gait data structure was best represented by an underlying continuum of data points based on at least three independent dimensions. Introduction: The diversity of gait deviations observed in children with CP has lead to repeated efforts to develop gait classification systems to assist in diagnosis, clinical-decision making and communication [1]. For these tools to be useful, they need to be able to differentiate gait patterns into distinct meaningful clinical categories [1]. Many attempts to classify gait previously have relied on methods that either arbitrarily or statistically allocate gait patterns into groups that may hold little clinical relevance [2]. Exploration of underlying structure of gait data and whether it can suitable by allocated into distinct clusters has received very little attention in previous literature of gait pattern classification. Patients/Materials and Methods: An expert panel of 24 clinicians, with an average of 10 years experience in instrumented gait analysis in children with CP, were invited to make judgements on the similarity of 48 different pairs of gait patterns using a
Figure 1. Configuration of the data on the first three dimensions. Discussion: The lack of clear clusters suggests that gait patterns from children with hCP appeared to lie more on a continuum of points rather than in distinct groups. These results suggest that gait classification for children with hemiplegia are more likely imposed on an underlying continuum of data structure rather that representing real distinct groups. This observation was however based on similarity scores from expert clinical judgments rather than absolute differences in gait curves. Further validation of this finding using quantifiable differences in gait curves is warranted. The structure of data identified in this investigation revealed that the kinematic gait data could be sufficiently represented by three to four dimensions allowing the complex nature of the data to be represented by a reduced number of dimensions that still adequately capture the variation in the data. This suggests that different approaches to describing gait patterns may be more appropriate and that the application of a score rather than group may better represent the continuum of data over the three to four independent dimensions. Further work is required to explore such dimensional scores.
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Abstracts of the 17th Annual Meeting of ESMAC, Poster Presentations / Gait & Posture 28S (2008) S49–S118
References [1] Dobson F, Morris ME, Baker R, Graham HK. Gait Posture 2006; 25: 140–152. [2] Chau T. Gait Posture 2001; 13: 46−66.
P060 Hemiplegic cerebral palsy: gait patterns and motor function across a population based cohort F. Dobson1 , H.K. Graham2 , R. Baker1 , M. Morris3 . 1 Hugh Williamson Gait Analysis Laboratory, Murdoch Children’s Research Institute; 2 Department of Orthopaedic Surgery, Royal Children’s Hospital; 3 School of Physiotherapy, The University of Melbourne, Australia Summary: This study examined the relationships between gait patterns and motor function classifications in a population-based population cohort of children with hemiplegic cerebral palsy (hCP) and compared the distribution of gait patterns in the current cohort to the original Winters Gage and Hicks (WGH) referralbased cohort. Conclusions: This first ever population based study of gait patterns in hCP achieved a recruitment rate of 71%. All but two of the 69 children could be categorised with the WGH classification. None were identified as WGH Group III. GMFCS level was associated with WGH type but there were only weak associations between categorisations of upper and lower limb function. Introduction: Even within well-defined subgroups of CP, such as spastic hemiplegia (hCP), there is marked variability in the presentation and function between individuals [1]. In response, several methods for differentiating individuals into more homogeneous subgroups have been developed. Previous attempts to classify gait patterns in CP predominately utilized cohorts that were referred to a service and as such, these classifications may not be representative of the total variation possible in the subtype(s) of the CP population. Patients/Materials and Methods: An attempt was made to include all eligible children identified with hCP on the Victorian Cerebral Palsy Register born in Victoria, Australia between the years 1990–1992. All children underwent standardised physical examination and 3-D gait analysis. Population characteristics were reported using the WGH classification for gait patterns [2]; the Manual Activity Classification System (MACS) [3] and House classification [4] for bilateral and unilateral upper limb function; and the Gross Motor Function Classification System (GMFCS) and Functional Mobility Scale (FMS) [5] for gross motor function. Results: A recruitment rate of 71% (69/97) was achieved. The majority of children (33%) demonstrated a WGH Group I gait pattern, 27% a Group II pattern, and 20% a Group IV pattern. Two gait patterns were non-classifiable using WGH descriptions. No gait patterns represented WGH Group III. The proportions of gait pattern groups between the current and original WGH cohorts were not significantly different [c2(d.f. = 2) = 1.47, p = 0.48]. All children were classified as GMFCS level I or II. Group I gait patterns were mostly classified as GMFCS level I (96%) whereas Group IV gait patterns were mostly classified as GMFCS level II (71%). A ceiling effect was observed on the FMS for the 5 and 50-metre distances, however there was greater variability of scores over the 500-metre distance. All children were classified as either MACS level 1, 2 or 3. Children with gait pattern groups I and II were mostly classified as MACS level 1. Children with Group
IV gait patterns were mostly classified as MACS level 2 or 3. Some children demonstrated mild involvement of the lower limb (i.e. WGH Group I) with more severe involvement of the upper limb (MACS level 3). All grades on the House classification were represented. There was only a weak association between lower limb involvement and upper limb involvement using the MACS (R = 0.49, p = 0.0001) and the House classification (R = 0.51, p < 0.0001). Discussion: This study confirmed that hCP generates a wide spectrum of variations. Many children with hCP demonstrated very high level of functioning and relatively low level of impairment. Most of these children were managed adequately in the community where access to physiotherapy, rehabilitation and orthotic management was appropriate and cost effective. A smaller number of individuals required more complex management where instrumented gait analysis was appropriate and beneficial to clinical decision making. The weak association between lower and upper limb involvement is consistent with the findings from previous studies [1] and supports the need for separate classification systems of upper limb and lower limb functioning in this group of children. Aside from the absence of WGH Group III gait patterns, the proportions and distributions of gait patterns were comparable between the current cohort and the original WGH cohort supporting the WGH system as a foundation for the development of any new or modified gait classifications in children with hCP. References [1] Damiano D, Abel M, Romness M, Oeffinger D, et al. (2006). Dev Med Child Neurol, 48(10), 797–803. [2] Winters F, Gage JR, Hicks R. J Bone Joint Surg 1987: 69-A(3): 437– 441. [3] Eliasson A-C, Krumlinde-Sundholm L, Rosblad B, et al. Dev Med Child Neurol 2006; 48: 549−54. [4] House JH, Gwathmey FW, Fidler MO. J Bone Joint Surg 1981; 63A: 216–225. [5] Graham HK, Harvey A, Rodda J, et al. J Pediatr Orthop 2004; 24: 514–520.
P061 Evaluating the outcome of single event multilevel surgery: find the way use the MAP (movement analysis profile) P. Thomason1 , X. Yu1 , R. Baker1 , H.K. Graham1 . 1 Gait CCRE Murdoch Children’s Research Institute, Royal Children’s Hospital, Australia Summary: The Movement Analysis Profile (MAP) is used to evaluate outcome following Single Event Multilevel Surgery (SEMLS) in a consecutive sample of 18 children with bilateral cerebral palsy (CP). Conclusions: The MAP illustrates overall improvement in gait following SEMLS and in individual kinematic parameters. Introduction: Several studies have found improvement after SEMLS using selected kinematic parameters [1−4] but only one [5] has done so using an overall index of gait pathology (the Gillette Gait Index, GGI). The aims of this study were to use the newly developed MAP to evaluate the outcome of SEMLS and the contribution of individual surgical procedures on outcome at 12 months post surgery. Patients/Materials and Methods: A consecutive sample of 18 children (11 male) with bilateral spastic CP, GMFCS Levels II