Parkinson's Disease Severity at 3 Years Can Be Predicted from Non-Motor Symptoms at Baseline Ayala, Alba Triviño Juárez, José Matías Forjaz, Maria João Rodríguez-Blázquez, Carmen Rojo-Abuin, José-Manuel Martínez-Martín, Pablo Parkinson's disease disease global severity predictive model Objective: 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. 2024-09-18T12:21:59Z 2024-09-18T12:21:59Z 2017-10-30 journal article Ayala A, Triviño-Juárez JM, Forjaz MJ, Rodríguez-Blázquez C, Rojo-Abuin JM, Martínez-Martín P. Parkinson's Disease Severity at 3 Years Can Be Predicted from Non-Motor Symptoms at Baseline. Front Neurol. 2017 Oct 30;8:551. doi: 10.3389/fneur.2017.00551. PMID: 29163328; PMCID: PMC5674937. https://hdl.handle.net/10481/94665 10.3389/fneur.2017.00551 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Frontiers Media S.A.