Session Information
Date: Thursday, June 23, 2016
Session Title: Parkinson's disease: Clinical trials, pharmacology and treatment
Session Time: 12:00pm-1:30pm
Location: Exhibit Hall located in Hall B, Level 2
Objective: To determine clinical and biological variables that predict time to initiation of symptomatic therapy (TIST) in de novo Parkinson’s disease (PD) patients.
Background: There is a substantial body of literature on the clinical predictors of the TIST in PD. However, there are limited data on the role of biological variables.
Methods: Parkinson’s Progression Markers Initiative (PPMI) is a longitudinal case-control study of de novo, untreated PD participants at enrollment. 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 TIST 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 & England activities of daily living scale, and The Movement Disorder Society Unified Parkinson’s disease Rating Scale (MDS-UPDRS) total score modestly predicted TIST (pseudo-R²=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 TIST was created using the linear and non-linear effects of the three top variables based on a post hoc Cox model.
Conclusions: Our findings using a novel machine learning method support previously reported clinical variables that predict TIST. 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.
To cite this abstract in AMA style:
T. Simuni, J. Long, C. Caspell-Garcia, C.S. Coffey, S. Lasch, C. Tanner, D. Jennings, K. Kieburtz, K. Marek, On behalf of the PPMI Investigators. Clinical and biological predictors of time to initiation of symptomatic therapy in early Parkinson’s disease [abstract]. Mov Disord. 2016; 31 (suppl 2). https://www.mdsabstracts.org/abstract/clinical-and-biological-predictors-of-time-to-initiation-of-symptomatic-therapy-in-early-parkinsons-disease/. Accessed November 8, 2026.« Back to 2016 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/clinical-and-biological-predictors-of-time-to-initiation-of-symptomatic-therapy-in-early-parkinsons-disease/