Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease

Abstract

Abstract Background: There are currently no methods to predict the development of levodopa-induced dyskinesia (LID), a frequent complication of Parkinson's disease (PD) treatment. Clinical predictors and single nucleotide polymorphisms (SNP) have been associated to LID in PD. Objective: To investigate the association of clinical and genetic variables with LID and to develop a diagnostic prediction model for LID in PD. Methods: We studied 430 PD patients using levodopa. The presence of LID was defined as an MDS-UPDRS Part IV score ≥1 on item 4.1. We tested the association between specific clinical variables and seven SNPs and the development of LID, using logistic regression models. Results: Regarding clinical variables, age of PD onset, disease duration, initial motor symptom and use of dopaminergic agonists were associated to LID. Only CC genotype of ADORA2A rs2298383 SNP was associated to LID after adjustment. We developed two diagnostic prediction models with reasonable accuracy, but we suggest that the clinical prediction model be used. This prediction model has an area under the curve of 0.817 (95% confidence interval [95%CI] 0.77‒0.85) and no significant lack of fit (Hosmer-Lemeshow goodness-of-fit test p=0.61). Conclusion: Predicted probability of LID can be estimated with reasonable accuracy using a diagnostic clinical prediction model which combines age of PD onset, disease duration, initial motor symptom and use of dopaminergic agonists.

Keywords

• dyskinesia
• Parkinson disease
• levodopa
• decision support techniques