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CC BY-NC-ND 4.0 · Arq Neuropsiquiatr 2020; 78(04): 214-216
DOI: 10.1590/0004-282X20190191
Article

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

Modelo de predição diagnóstica para discinesias induzidas por levodopa na doença de Parkinson
Bruno Lopes SANTOS-LOBATO 
1   Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Neurociências e Ciências Comportamentais, Ribeirão Preto SP, Brazil.
2   Universidade de São Paulo, Núcleo de Apoio à Pesquisa em Neurociência Aplicada, São Paulo SP, Brazil.
,
Artur F. SCHUMACHER-SCHUH 
3   Hospital de Clínicas de Porto Alegre, Porto Alegre RS, Brazil.
,
Carlos R. M. RIEDER 
3   Hospital de Clínicas de Porto Alegre, Porto Alegre RS, Brazil.
,
Mara H. HUTZ 
4   Universidade Federal do Rio Grande do Sul, Departamento de Genética, Porto Alegre RS, Brazil.
,
Vanderci BORGES 
5   Universidade Federal de São Paulo, Departamento de Neurologia, São Paulo SP, Brazil.
,
Henrique Ballalai FERRAZ 
5   Universidade Federal de São Paulo, Departamento de Neurologia, São Paulo SP, Brazil.
,
Ignacio F. MATA 
6   Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA.
7   University of Washington, Department of Neurology, Seattle, WA, USA.
,
Cyrus P. ZABETIAN 
6   Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA.
7   University of Washington, Department of Neurology, Seattle, WA, USA.
,
Vitor TUMAS 
1   Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Neurociências e Ciências Comportamentais, Ribeirão Preto SP, Brazil.
2   Universidade de São Paulo, Núcleo de Apoio à Pesquisa em Neurociência Aplicada, São Paulo SP, Brazil.
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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.

Resumo

Introdução: No momento, não há métodos para se predizer o desenvolvimento de discinesias induzidas por levodopa (DIL), uma frequente complicação do tratamento da doença de Parkinson (DP). Preditores clínicos e polimorfismos de nucleotídeo único (SNP) têm sido associados às DIL na DP. Objetivo: Investigar a associação entre variáveis clínicas e genéticas com as DIL e desenvolver um modelo de predição diagnóstica de DIL na DP. Métodos: Foram avaliados 430 pacientes com DP em uso de levodopa. A presença de DIL foi definida como escore ≥1 no item 4.1 da MDS-UPDRS Parte IV. Nós testamos a associação entre variáveis clínicas específicas e sete SNPs com o desenvolvimento de DIL, usando modelos de regressão logística. Resultados: Em relação às variáveis clínicas, idade de início da doença, duração da doença, sintomas motores iniciais e uso de agonistas dopaminérgicos estiveram associados às DIL. Apenas o genótipo CC do SNP rs2298383 no gene ADORA2A esteve associado com DIL após o ajuste. Nós desenvolvemos dois modelos preditivos diagnósticos com acurácia razoável, mas sugerimos o uso do modelo preditivo clínico. Esse modelo de predição tem uma área sob a curva de 0,817 (intervalo de confiança de 95% [IC95%] 0,77–0,85) e sem perda significativa de ajuste (teste de qualidade de ajuste de Hosmer-Lemeshow p=0,61). Conclusão: A probabilidade prevista de DIL pode ser estimada, com acurácia razoável, por meio do uso de um modelo preditivo diagnóstico clínico, que combina a idade de início da doença, duração da doença, sintomas motores iniciais e uso de agonistas dopaminérgicos.

Keywords:

dyskinesia - Parkinson disease - levodopa - decision support techniques

Palavras-chave:

discinesias - doença de Parkinson - levodopa - técnicas de apoio para a decisão

Support:

The present study was supported by the Parkinson’s Disease Foundation. One author (B.L.S.L.) received an MDS-PAS Visiting Trainee Grant from the International Parkinson and Movement Disorder Society for personal costs support. This material is the result of work supported by resources and the use of facilities at the VA Puget Sound Health Care System.




Publication History

Received: 13 October 2019

Accepted: 10 November 2019

Article published online:
13 June 2023

© 2020. Academia Brasileira de Neurologia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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