2026-06-17T21:16:56Zhttps://riubu.ubu.es/oai/request

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

Parkinson’s Disease Severity at 3 Years Can Be Predicted from Non-Motor Symptoms at Baseline Ayala, Alba Triviño Juárez, José Matías Forjaz, María Joao Rodríguez Blázquez, Carmen Rojo Abuin, José Manuel Martínez Martín, Pablo Cubo Delgado, Esther ELEP Group Parkinson’s disease Disease global severity Predictive model Multilevel analysis Multiple imputation 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. 2024-03-08T08:57:26Z 2024-03-08T08:57:26Z 2024-03-08T08:57:26Z 2017-10 info:eu-repo/semantics/article http://hdl.handle.net/10259/8780 10.3389/fneur.2017.00551 1664-2295 eng Frontiers in Neurology. 2017, V. 8 https://doi.org/10.3389/fneur.2017.00551 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Frontiers Media