Detecting post-operative change in gait function using principal component analysis in subjects with cerebral palsy

Principal components analysis is a multivariate statistical method that has been used in gait analysis. One example of use of the method is the production of The Gillette Gait Index. This index, indicating normality in gait function, has been presented and validated by previous authors. According to suggestions made by these authors, the index could potentially be used to evaluate change in gait function after surgical intervention in subjects with cerebral palsy. The Gillette Gait Index was calculated using principal components analysis for nineteen individuals with cerebral palsy (5 hemiplegics, 13 diplegics and 1 quadriplegic; mean age 16 years, range 10-31 years) in a retrospective study. The change in index value per individual from the pre- to the postoperative situations was compared to the evaluation of change made by an experienced clinician. Agreement was evaluated using Cohen´s kappa ( k ), resulting in a value of k=0.406, which is usually considered to be a fair to moderate level of agreement. Published poste