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Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale
Item response theory (IRT) has been recently adopted to successfully characterize the progression of Parkinson's disease using serial Unified Parkinson's Disease Rating Scale (UPDRS) measurements. However, it has yet to be applied in predicting the longitudinal changes of levodopa dose req...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213413/ https://www.ncbi.nlm.nih.gov/pubmed/33939329 http://dx.doi.org/10.1002/psp4.12632 |
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author | Chae, Dongwoo Chung, Su Jin Lee, Phil Hyu Park, Kyungsoo |
author_facet | Chae, Dongwoo Chung, Su Jin Lee, Phil Hyu Park, Kyungsoo |
author_sort | Chae, Dongwoo |
collection | PubMed |
description | Item response theory (IRT) has been recently adopted to successfully characterize the progression of Parkinson's disease using serial Unified Parkinson's Disease Rating Scale (UPDRS) measurements. However, it has yet to be applied in predicting the longitudinal changes of levodopa dose requirements in the real‐world setting. Here we use IRT to extract two latent variables that represent tremor and non‐tremor‐related symptoms from baseline assessments of UPDRS Part III scores. We show that relative magnitudes of the two latent variables are strong predictors of the progressive increase of levodopa equivalent dose (LED). Retrospectively collected item‐level UPDRS Part III scores and longitudinal records of prescribed medication doses of 128 patients with de novo PD extracted from the electronic medical records were used for model building. Supplementary analysis based on a subset of 36 patients with at least three serial assessments of UPDRS Part III scores suggested that the two latent variables progress at significantly different rates. A web application was developed to facilitate the use of our model in making individualized predictions of future LED and disease progression. |
format | Online Article Text |
id | pubmed-8213413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82134132021-06-28 Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale Chae, Dongwoo Chung, Su Jin Lee, Phil Hyu Park, Kyungsoo CPT Pharmacometrics Syst Pharmacol Research Item response theory (IRT) has been recently adopted to successfully characterize the progression of Parkinson's disease using serial Unified Parkinson's Disease Rating Scale (UPDRS) measurements. However, it has yet to be applied in predicting the longitudinal changes of levodopa dose requirements in the real‐world setting. Here we use IRT to extract two latent variables that represent tremor and non‐tremor‐related symptoms from baseline assessments of UPDRS Part III scores. We show that relative magnitudes of the two latent variables are strong predictors of the progressive increase of levodopa equivalent dose (LED). Retrospectively collected item‐level UPDRS Part III scores and longitudinal records of prescribed medication doses of 128 patients with de novo PD extracted from the electronic medical records were used for model building. Supplementary analysis based on a subset of 36 patients with at least three serial assessments of UPDRS Part III scores suggested that the two latent variables progress at significantly different rates. A web application was developed to facilitate the use of our model in making individualized predictions of future LED and disease progression. John Wiley and Sons Inc. 2021-05-03 2021-06 /pmc/articles/PMC8213413/ /pubmed/33939329 http://dx.doi.org/10.1002/psp4.12632 Text en © 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Chae, Dongwoo Chung, Su Jin Lee, Phil Hyu Park, Kyungsoo Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale |
title | Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale |
title_full | Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale |
title_fullStr | Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale |
title_full_unstemmed | Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale |
title_short | Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale |
title_sort | predicting the longitudinal changes of levodopa dose requirements in parkinson’s disease using item response theory assessment of real‐world unified parkinson's disease rating scale |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213413/ https://www.ncbi.nlm.nih.gov/pubmed/33939329 http://dx.doi.org/10.1002/psp4.12632 |
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