Discrimination between hereditary spastic paraplegia and cerebral palsy based on gait analysis data: A machine...

There is no current consensus on how to differentiate between hereditary spastic paraplegia and spastic cerebral palsy on the basis of clinical presentation. Several previous studies have investigated differences in kinematic parameters obtained from clinical gait analysis. None have attempted to combine multiple gait and physical exam measures to discriminate between these two diagnoses. This study aims to investigate the ability of a machine learning approach using data from clinical gait analysis to differentiate these cohorts.

A retrospective analysis of a gait database compiled a dataset of 179 gait and physical exam variables from 28 individuals (62 analyses) diagnosed with hereditary spastic paraplegia and 678 (1504 analyses) with bilateral spastic cerebral palsy. This data was used in a Bayesian additive regression tree (BART) analysis classified by medical record diagnosis. A 10-fold cross validation generated probabilistic distribution that each analysis was from an individual carrying the hereditary spastic paraplegia diagnosis. A diagnostic probability cutoff threshold balanced type I and type II errors. Predicted versus actual diagnoses were classified into a contingency table.

The algorithm was able to correctly classify the two diagnoses with 91% specificity and 90% sensitivity.

A machine learning approach using data from clinical gait analysis was able to distinguish participants with hereditary spastic paraplegia from those with bilateral spastic cerebral palsy with high specificity and sensitivity. This algorithm can be used to assess if individuals seen for gait disorders who do not yet have a definitive diagnosis have characteristics associated with hereditary spastic paraplegia. The results of the model inform the decision to suggest genetic testing to either confirm or refute the diagnosis of hereditary spastic paraplegia.