Ayala, AlbaTriviño-Juárez, José MatíasForjaz, Maria JoãoRodríguez-Blázquez, CarmenRojo-Abuin, Jose ManuelMartínez-Martín, PabloELEP Project2022-05-252022-05-252017-10-30Front Neurol. 2017 Oct 30;8:551.1664-2295http://hdl.handle.net/20.500.12105/14523Objective: The 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). Methods: This 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. Results: The 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. Conclusion: Disease 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.engVoRParkinson’s diseaseDisease global severityMultilevel analysisMultiple imputationPredictive modelAtribución 4.0 Internacional29163328855110.3389/fneur.2017.00551Frontiers in Neurologyopen access