2026-09-06T14:15:19Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/87802024-03-09T01:05:25Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

00925njm 22002777a 4500 dc Ayala, Alba author Triviño Juárez, José Matías author Forjaz, María Joao author Rodríguez Blázquez, Carmen author Rojo Abuin, José Manuel author Martínez Martín, Pablo author Cubo Delgado, Esther author ELEP Group author 2017-10 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. http://hdl.handle.net/10259/8780 10.3389/fneur.2017.00551 1664-2295 Parkinson’s disease Disease global severity Predictive model Multilevel analysis Multiple imputation Parkinson’s Disease Severity at 3 Years Can Be Predicted from Non-Motor Symptoms at Baseline