Detection of Parkinson's Disease Early Progressors Using Routine Clinical Predictors

Abstract

Parkinson's disease (PD) is a progressive, neurodegenerative disease characterised by the presence of motor and non-motor symptoms and signs. The symptoms of PD tend to begin very gradually and then become progressively more severe. The rate of PD progression is hard to predict and is different from one person to another. Namely, while in some patients the disease develops fast in just a few years from the diagnosis, in some the disease takes a more idle course and progresses slowly. We aimed to identify patients that develop severe motor symptoms within four years from PD diagnosis (early progressors) and separate them from those in whom severe symptoms develop beyond this point. We used data from the Parkinson’s Progression Markers Initiative (PPMI) dataset to calculate motor progression of the disease by the use of motor scores as assessed by MDS-UPDRS III. The predictors were defined as baseline scores of selected clinical variables and the difference between motor scores at 1-year after enrolment in the study and the same scores at baseline. The rationale for predictor selection was that they should be readily available in routine clinical practice. We tested four different classifiers: logistic regression, decision tree, random forest, and gradient boosting. The best performing classifier was the logistic regression with an area under the ROC curve of 81%. We believe this can be the basis for a reliable and explainable classifier, using only standard clinical variables, for identifying early progressors with high recall (80%) three years in advance.