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

Detalhes bibliográficos
Autor(a) principal: SANTOS-LOBATO,Bruno Lopes
Data de Publicação: 2020
Outros Autores: SCHUMACHER-SCHUH,Artur F., RIEDER,Carlos R. M., HUTZ,Mara H., BORGES,Vanderci, FERRAZ,Henrique Ballalai, MATA,Ignacio F., ZABETIAN,Cyrus P., TUMAS,Vitor
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Arquivos de neuro-psiquiatria (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X2020000400206
Resumo: 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.
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spelling Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s diseasedyskinesiaParkinson diseaselevodopadecision support techniquesAbstract 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.Academia Brasileira de Neurologia - ABNEURO2020-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X2020000400206Arquivos de Neuro-Psiquiatria v.78 n.4 2020reponame:Arquivos de neuro-psiquiatria (Online)instname:Academia Brasileira de Neurologiainstacron:ABNEURO10.1590/0004-282x20190191info:eu-repo/semantics/openAccessSANTOS-LOBATO,Bruno LopesSCHUMACHER-SCHUH,Artur F.RIEDER,Carlos R. M.HUTZ,Mara H.BORGES,VanderciFERRAZ,Henrique BallalaiMATA,Ignacio F.ZABETIAN,Cyrus P.TUMAS,Vitoreng2020-04-27T00:00:00Zoai:scielo:S0004-282X2020000400206Revistahttp://www.scielo.br/anphttps://old.scielo.br/oai/scielo-oai.php||revista.arquivos@abneuro.org1678-42270004-282Xopendoar:2020-04-27T00:00Arquivos de neuro-psiquiatria (Online) - Academia Brasileira de Neurologiafalse
dc.title.none.fl_str_mv Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
title Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
spellingShingle Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
SANTOS-LOBATO,Bruno Lopes
dyskinesia
Parkinson disease
levodopa
decision support techniques
title_short Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
title_full Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
title_fullStr Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
title_full_unstemmed Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
title_sort Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
author SANTOS-LOBATO,Bruno Lopes
author_facet SANTOS-LOBATO,Bruno Lopes
SCHUMACHER-SCHUH,Artur F.
RIEDER,Carlos R. M.
HUTZ,Mara H.
BORGES,Vanderci
FERRAZ,Henrique Ballalai
MATA,Ignacio F.
ZABETIAN,Cyrus P.
TUMAS,Vitor
author_role author
author2 SCHUMACHER-SCHUH,Artur F.
RIEDER,Carlos R. M.
HUTZ,Mara H.
BORGES,Vanderci
FERRAZ,Henrique Ballalai
MATA,Ignacio F.
ZABETIAN,Cyrus P.
TUMAS,Vitor
author2_role author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv SANTOS-LOBATO,Bruno Lopes
SCHUMACHER-SCHUH,Artur F.
RIEDER,Carlos R. M.
HUTZ,Mara H.
BORGES,Vanderci
FERRAZ,Henrique Ballalai
MATA,Ignacio F.
ZABETIAN,Cyrus P.
TUMAS,Vitor
dc.subject.por.fl_str_mv dyskinesia
Parkinson disease
levodopa
decision support techniques
topic dyskinesia
Parkinson disease
levodopa
decision support techniques
description 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.
publishDate 2020
dc.date.none.fl_str_mv 2020-04-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X2020000400206
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X2020000400206
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0004-282x20190191
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Academia Brasileira de Neurologia - ABNEURO
publisher.none.fl_str_mv Academia Brasileira de Neurologia - ABNEURO
dc.source.none.fl_str_mv Arquivos de Neuro-Psiquiatria v.78 n.4 2020
reponame:Arquivos de neuro-psiquiatria (Online)
instname:Academia Brasileira de Neurologia
instacron:ABNEURO
instname_str Academia Brasileira de Neurologia
instacron_str ABNEURO
institution ABNEURO
reponame_str Arquivos de neuro-psiquiatria (Online)
collection Arquivos de neuro-psiquiatria (Online)
repository.name.fl_str_mv Arquivos de neuro-psiquiatria (Online) - Academia Brasileira de Neurologia
repository.mail.fl_str_mv ||revista.arquivos@abneuro.org
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