Prediction of the Gross Motor Function Measure-66 in Ambulant Children with Cerebral Palsy Based on Instrumental Gait Analysis Using Machine-Learning Algorithms

The Gross Motor Function Measure-66 (GMFM-66, range of values: 0 to 100 points) is one of the most widely used clinical tests to quantify motor function in children with cerebral palsy (CP). A disadvantage of the GMFM-66 is that it can take up to one hour to complete. The aim of the study was to evaluate whether the GMFM-66 can be predicted with sufficient accuracy by the results of an instrumental gait analysis (IGA) in ambulant children with CP. A retrospective analysis was conducted on n = 256 ambulant children with CP enrolled in a rehabilitation program between 2018 and 2023. The sample consisted of 97 females and 159 males, with a mean age of 9.0 years (SD 3.6 years). The IGA was performed with a Zebris FDM pressure plate. For the prediction of the GMFM-66, different statistical models were used (multiple linear regression and machine learning algorithms). Among the models considered, the XGBoost model had the best predictive performance (mean absolute error 6.32 (95%CI 5.35–7.28)). Agreement between results from gait analyses by the Zebris FDM pressure plate and GMFM-66 is not yet sufficient to predict the GMFM-66 score with acceptable accuracy for clinical purposes.