S42 ESMAC 2012 abstract / Gait & Posture 38 (2013) S1–S116

FDO status. The RF classifier was built using standard techniques,including model reduction based on variable importance [4]. Limbspredicted to be +FDO were labeled as having met historic criteria,those predicted to be −FDO failed historic criteria.

Results: The RF classifier identified the historic criteria excep-tionally well, based on commonly used classifier statistics [Table 1].There were 31 variables included in the final model. These could bereasonably grouped into the following categories: prior FDO, age,anteversion by physical exam, hip rotation, and foot progression.

Discussion and conclusions: The RF algorithm accurately iden-tified historic selection criteria used at one center to decide onincluding FDO as part of a SEMLS. The criteria variables and lev-els are now rigorously defined so that limbs and outcomes can beevaluated against an established 18 year treatment standard. Thecriteria also allow for objective testing of alternative criteria.

References

[1] Arnold AS, et al. Developmental Medicine and Child Neurology 1997;39:40–42.[2] Õunpuu S, et al. Journal of Pediatric Orthopaedics 2002;22:139–45.[3] Dreher T, et al. Gait and Posture 2007;26:25–31.[4] Breiman L. Machine Learning 2001;45:15–32.

http://dx.doi.org/10.1016/j.gaitpost.2013.07.083

O70

Patterns of historical outcomes for femoralderotational osteotomy revealed by theRandom Forest algorithm

Michael H. Schwartz, Adam Rozumalski, Tom F.Novacheck

Gillette Children’s Specialty Healthcare, Center forGait and Motion Analysis, St. Paul, United States

Introduction: Femoral derotational osteotomies (FDO) arecommonly performed on children with cerebral palsy, yet thereare no widely accepted selection criteria for this surgery [1–3]. TheRandom Forest (RF) algorithm is a machine learning method thathas been used to identify historical limb selection criteria for FDO.This study examines the presentation and gait outcomes for limbsmeeting the FDO criteria.

Patients/materials and methods: In a related study the RF clas-sifier was applied to 800 limbs that had undergone single-eventmulti-level surgery (SEMLS). An accurate criteria for predictingwhich limbs would receive an FDO was found. Multi-dimensionalscaling (MDS) was applied to the RF’s proximity matrix to reducethe dimensionality of the criteria from 31 variables to 2 [4,5]. Limbs

meeting criteria were retained, and a K-means cluster analysisidentified groups with similar MDS characteristics.

Results: The clusters could be aptly described as (1) exessiveanteversion and internal rotation, (2) mixed levels of anteversionand internal rotation, and (3) excessive anteversion but no internalrotation [Fig. 1]. Outcomes for the three groups showed signifi-cant GDI changes after surgery [Table 1]. Cluster 1 improved themost, while cluster 3 improved the least. The lack of GDI improve-ment in cluster 3 was associated with a significant worsening offoot progression.

Discussion and conclusions: The data show that if transverseplane alignment is an important outcome, FDO should be reservedfor limbs with both excessive anteversion and internal rotationgait. Limbs with only anteversion are at risk of acquiring iatrogenicpathological external foot progression.

References

[1] Staheli LT. Clinical Orthopaedics and Related Research 1989;247:61–6.[2] Ounpuu S, et al. Journal of Pediatric Orthopaedics 2002;22:139–45.[3] Dreher T, et al. Gait and Posture 2007;26:25–31.[4] Brieman L. Machine Learning 2001;45:5–32.[5] Cox TF, Cox MAA. Multidimensional scaling. Chapman and Hall; 2001.

http://dx.doi.org/10.1016/j.gaitpost.2013.07.084

O71

The influence of muscle strength on kinematicgait deviations is similar across patients withvarious pathologies

Katrin Schweizer, Jacqueline Romkes, ReinaldBrunner

University Children’s Hospital Basel (UKBB),Laboratory for Movement Analysis, Basel,Switzerland

Introduction: At present, patients with walking restrictionsare treated according to their primary pathology. However, if theassociation between muscle strength and kinematic gait devi-ations behave similar in patients with various pathologies, itmight be more efficient to base therapy on the biomechanicalconstraints.

Patients/materials and methods: All data of patients whowere assessed by clinical gait analysis (VICON) and manual mus-cle strength (MMS) testing in our gait laboratory over the last12 years were included. The patients walked barefoot at a self-selected speed. The included 716 patients were clustered into seven

Fig. 1. K-means cluster a nafysis identifies three distinct groups of limbs. Limbs in clusters land 3 all underwent FDO, while those in cluster 2 had a mix of FDO status limbs,Cluster 1 presented with anteversion and internal rotation gait, while duster 3 had anteversion only.

ESMAC 2012 abstract / Gait & Posture 38 (2013) S1–S116 S43

Fig. 1. Presents the mean manual muscle strength (MMS) versus the Gait Profile Score (GPS) in the different patient groups. The gray band represents 95-percentile of thenorm.

Table 1Shows the difference, of the GPS offset of Ouni to the other patient groups at a MMSof 5.

Patient group Intercept ± SE p

OUni (reference) 5–0 ± 0–7 —NspUni 0–4 ± 0–4 0.351NflaUni 2.9 ± 1.1 0.006OBi 0.2 ± 0.3 0.557KspBi 1.6 ± 0.4 <0.00lNipBiNTC 2.5 ± 0.6 <0.001NflaBi L6 ± 0–5 0.002

groups: Orthopaedic uni-/bilateral (OUni/OBi); neurological flacciduni-bilateral (NflaUni/NflaBi); neurological spastic uni-/bilateralwith/without adequate trunk control (NspUni/NspBi/NspBiNTC).The gait profile score (GPS) [1] was calculated. The mean MMS,derived by clinical testing [2], was the mean over all leg musclegroups. General least square models (software R 2.12.0) were usedto assess whether the effects of MMS on GPS differ among patientgroups. The GPS offsets at a MMS of 5 were compared betweenOUni and the other patient groups (Fig. 1).

Results: MMS had a strong, negative effect on GPS score (MMS:−3.0 ± 0.2, p < .001). There were no significant differences in thiseffect among patient groups (p = .848). However, they strongly dif-fered in GPS offset at a MMS of 5 (p < .001) (Table 1).

Discussion and conclusions: The amount of gait deviationincreased with the loss of muscle strength, and this behaviourseemed to be independent of the pathology. The severity of thepathology was reflected in the higher GPS (i.e. kinematic deviation)at normal MMS. Note that NflaUni is an exception, possibly biasedby the small patient number (n = 12). We conclude that gait devia-tions mainly result from muscle weakness. This knowledge can findclinical implication in treatment planning and in the interpretationof gait compensations in patients with various constraints. The roleof the basic disease should not be overemphasised.

References

[1] Baker R, et al. Gait and Posture 2009:30.[2] Hislop HJ et al., 1999, Manuelle Muskeltests. SNF project 32003B 127534.

http://dx.doi.org/10.1016/j.gaitpost.2013.07.085

O72

The influence of walking speed on the GaitDeviation Index in individuals with RheumatoidArthritis

Anna-Clara Esbjörnsson 1, Adam Rozumalski 2,Maura D. Iversen 3, Michael H. Schwartz 2, Eva W.Broström 1

1 Karolinska Insitutet, Department of Women’s andChildren’s Health, Stockholm, Sweden2 Gillette Children’s Specialty Healthcare, St Paul,MD, United States3 Brigham & Women’s Hospital, NortheasternUniversity and Division of Rheumatology, St Paul,MA, United States

Introduction: Decreased walking speed reduces joint rotationsin healthy populations. Individuals with Rheumatoid Arthritis (RA)exhibit decreased walking speed and joint rotations, however,debate exists over the influence of walking speed versus pathologyon gait deviations in RA. In 2008 the Gait Deviation Index (GDI) waspublished [1] and in 2011 Rozumalski and Schwartz demonstratedthat GDI was influenced by walking speed in a healthy popula-tion particularly at slow or fast speeds [2]. Individuals with RAdemonstrate reduced GDI scores and walking speed as comparedto healthy individuals. Therefore, the aim of this study was to eval-uate the impact of walking speed on the GDI in individuals withRA.

Patients/materials and methods: Sixty-three patients with RA(mean age (SD) 57(13) yrs) and 59 age matched adults (mean age(SD) 54 (15) were evaluated retrospectively. Three-dimensionallower extremity joint kinematics and stride parameters of inde-pendent barefoot walking at self-selected speed were collected.Two sets of GDI were calculated (free speed (GDI)[1] and speedmatched (SMGDI) [2]) for each individual using two different ref-erences. The free speed GDI used a reference consisting of free-speed trials from the control group [1]. The speed-matched GDIused a reference matched to the stride speed for which the GDIwas being calculated [2].

Results: The average non-dimensional self-selected walkingspeed for the control group was 0.47 and for the RA group 0.34. Forindividuals with RA, mean SMGDI-scores were 4 GDI units highercompared to mean GDI-scores (p = 0.017), no difference was seenbetween the scores in healthy subjects. Furthermore, the differ-ence between GDI-scores and SMGDI-scores tends to be greaterthe slower the individual walks. However, the mean SMGDI-scoresfor the individuals with RA (91.7 (9.0)) were still significantly lowercompared to control subjects (99.9 (8.6)) (p < 0,001).