Journal article
Predictors of time to initiation of symptomatic therapy in early Parkinson's disease
Annals of clinical and translational neurology, Vol.3(7), pp.482-494
07/2016
DOI: 10.1002/acn3.317
PMCID: PMC4931714
PMID: 27386498
Abstract
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‐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 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.
Details
- Title: Subtitle
- Predictors of time to initiation of symptomatic therapy in early Parkinson's disease
- Creators
- Tanya Simuni - Northwestern UniversityJeffrey D Long - University of IowaChelsea Caspell‐Garcia - College of Public HealthChristopher S Coffey - College of Public HealthShirley Lasch - LLC (MNI)Caroline M Tanner - University of CaliforniaDanna Jennings - LLC (MNI)Karl D Kieburtz - University of RochesterKenneth Marek - LLC (MNI)the PPMI Investigators
- Resource Type
- Journal article
- Publication Details
- Annals of clinical and translational neurology, Vol.3(7), pp.482-494
- DOI
- 10.1002/acn3.317
- PMID
- 27386498
- PMCID
- PMC4931714
- NLM abbreviation
- Ann Clin Transl Neurol
- ISSN
- 2328-9503
- eISSN
- 2328-9503
- Number of pages
- 13
- Grant note
- Avid Radiopharmaceuticals Eli Lilly & Co. Pfizer Bristol‐Myers Squibb Genentech Biogen Idec Michael J. Fox Foundation for Parkinson's Research (MJFF) Piramal UCB GE Healthcare Abbvie GlaxoSmithKline MesoScale Covance F. Hoffman‐La Roche, Ltd. Lundbeck Merck
- Language
- English
- Date published
- 07/2016
- Academic Unit
- Psychiatry; Biostatistics
- Record Identifier
- 9983997370002771
Metrics
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