parkinson’s disease severity at 3 years can be predicted from non-motor symptoms at baseline
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2017
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Abstract
ObjectiveThe aim of this study is to present a predictive model of Parkinson’s disease (PD) global severity, measured with the Clinical Impression of Severity Index for Parkinson’s Disease (CISI-PD).MethodsThis is an observational, longitudinal study with annual follow-up assessments over 3 years (four time points). A multilevel analysis and multiple imputation techniques were performed to generate a predictive model that estimates changes in the CISI-PD at 1, 2, and 3 years.ResultsThe clinical state of patients (CISI-PD) significantly worsened in the 3-year follow-up. However, this change was of small magnitude (effect size: 0.44). The following baseline variables were significant predictors of the global severity change: baseline global severity of disease, levodopa equivalent dose, depression and anxiety symptoms, autonomic dysfunction, and cognitive state. The goodness-of-fit of the model was adequate, and the sensitive analysis showed that the data imputation method applied was suitable.ConclusionDisease progression depends more on the individual’s baseline characteristics than on the 3-year time period. Results may contribute to a better understanding of the evolution of PD including the non-motor manifestations of the disease.
| Reference Key |
ayala2017frontiersparkinsons
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| Authors | ;Alba Ayala;José Matías Triviño-Juárez;Maria João Forjaz;Carmen Rodríguez-Blázquez;José-Manuel Rojo-Abuin;Pablo Martínez-Martín |
| Journal | journal of photochemistry and photobiology a: chemistry |
| Year | 2017 |
| DOI |
10.3389/fneur.2017.00551
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