Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , |
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. |
id |
ABNEURO-1_426aedfab4ed808aff3d3cfa202bdb76 |
---|---|
oai_identifier_str |
oai:scielo:S0004-282X2020000400206 |
network_acronym_str |
ABNEURO-1 |
network_name_str |
Arquivos de neuro-psiquiatria (Online) |
repository_id_str |
|
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 |
_version_ |
1754212787341492224 |