Objective: To determine clinical and biological variables that predict time to initiation of symptomatic therapy in de novo Parkinson's disease patients.
Methods: Parkinson's Progression Markers Initiative is a longitudinal case-control study of de novo, untreated Parkinson's disease participants at enrolment. Participants contribute a wide range of motor and non-motor measures, including biofluids and imaging biomarkers. The machine learning method of random survival forests was used to examine the ability of baseline variables to predict time to initiation of symptomatic therapy since study enrollment (baseline).
Results: There were 423 PD participants enrolled in PPMI and 33 initial baseline variables. Cross-validation results showed that the three-predictor subset of disease duration (time from diagnosis to enrollment), the modified Schwab and England activities of daily living scale, and the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score modestly predicted time to initiation of symptomatic therapy (C = 0.70, pseudo-R (2) = 0.13). Prediction using the three variables was similar to using the entire set of 33. None of the biological variables increased accuracy of the prediction. A prognostic index for time to initiation of symptomatic therapy was created using the linear and nonlinear effects of the three top variables based on a post hoc Cox model.
Interpretation: Our findings using a novel machine learning method support previously reported clinical variables that predict time to initiation of symptomatic therapy. However, the inclusion of biological variables did not increase prediction accuracy. Our prognostic index constructed, based on the group-level survival curve can provide an indication of the risk of initiation of ST for PD patients based on functions of the three top predictors.
Keywords: Biomarkers; Parkinson's disease; symptomatic therapy.