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A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data
INTRODUCTION: Claims databases are preferred for research on multiple sclerosis (MS) as this condition is characterized by low prevalence and long disease course. However, Japanese claims databases contain no information on disease severity or disability status of MS. Here, we aimed to explore the p...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Healthcare
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857383/ https://www.ncbi.nlm.nih.gov/pubmed/35064908 http://dx.doi.org/10.1007/s40120-022-00324-0 |
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author | Kawachi, Izumi Otaka, Hiromichi Iwasaki, Kosuke Takeshima, Tomomi Ueda, Kengo |
author_facet | Kawachi, Izumi Otaka, Hiromichi Iwasaki, Kosuke Takeshima, Tomomi Ueda, Kengo |
author_sort | Kawachi, Izumi |
collection | PubMed |
description | INTRODUCTION: Claims databases are preferred for research on multiple sclerosis (MS) as this condition is characterized by low prevalence and long disease course. However, Japanese claims databases contain no information on disease severity or disability status of MS. Here, we aimed to explore the possibility of utilizing a principal component analysis (PCA) to estimate MS severity using a Japanese claims database. METHODS: An MS severity score was developed using a PCA. Factors related to functional systems for Expanded Disability Status Scale (EDSS) and higher disease severity (74 diagnoses, 68 drug prescriptions, and 77 procedures) were extracted from the claims database (April 2008–August 2018). The score (PC1 score) was developed for each patient-year—each year from the first diagnosis (excluding the year of the first diagnosis), based on the first principal component of the included factors. Finally, the patient-years were classified into quartiles based on the PC1 score, and demographic information and medical status were analyzed. RESULTS: The database contained 7067 patients with MS. The highest score group had a higher mean age (55.4 ± 0.2 [mean ± standard error] years), lower percentage of women (64.4 ± 0.7%), and longer mean disease duration from first diagnosis (8.1 ± 0.1 years) than the lowest score group (43.3 ± 0.2 years, 68.4 ± 0.8%, and 6.0 ± 0.1 years, respectively). In addition, the PC1 score of each patient positively correlated with disease duration from diagnosis. CONCLUSION: We developed a PC1 score to indicate MS severity using information from a Japanese claims database. Since changes in demographic features we observed are consistent with findings of previous research, this score might represent MS severity to some extent. Further research is necessary to validate this score with clinical measurement of disability such as the EDSS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40120-022-00324-0. |
format | Online Article Text |
id | pubmed-8857383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
spelling | pubmed-88573832022-02-23 A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data Kawachi, Izumi Otaka, Hiromichi Iwasaki, Kosuke Takeshima, Tomomi Ueda, Kengo Neurol Ther Original Research INTRODUCTION: Claims databases are preferred for research on multiple sclerosis (MS) as this condition is characterized by low prevalence and long disease course. However, Japanese claims databases contain no information on disease severity or disability status of MS. Here, we aimed to explore the possibility of utilizing a principal component analysis (PCA) to estimate MS severity using a Japanese claims database. METHODS: An MS severity score was developed using a PCA. Factors related to functional systems for Expanded Disability Status Scale (EDSS) and higher disease severity (74 diagnoses, 68 drug prescriptions, and 77 procedures) were extracted from the claims database (April 2008–August 2018). The score (PC1 score) was developed for each patient-year—each year from the first diagnosis (excluding the year of the first diagnosis), based on the first principal component of the included factors. Finally, the patient-years were classified into quartiles based on the PC1 score, and demographic information and medical status were analyzed. RESULTS: The database contained 7067 patients with MS. The highest score group had a higher mean age (55.4 ± 0.2 [mean ± standard error] years), lower percentage of women (64.4 ± 0.7%), and longer mean disease duration from first diagnosis (8.1 ± 0.1 years) than the lowest score group (43.3 ± 0.2 years, 68.4 ± 0.8%, and 6.0 ± 0.1 years, respectively). In addition, the PC1 score of each patient positively correlated with disease duration from diagnosis. CONCLUSION: We developed a PC1 score to indicate MS severity using information from a Japanese claims database. Since changes in demographic features we observed are consistent with findings of previous research, this score might represent MS severity to some extent. Further research is necessary to validate this score with clinical measurement of disability such as the EDSS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40120-022-00324-0. Springer Healthcare 2022-01-22 /pmc/articles/PMC8857383/ /pubmed/35064908 http://dx.doi.org/10.1007/s40120-022-00324-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Original Research Kawachi, Izumi Otaka, Hiromichi Iwasaki, Kosuke Takeshima, Tomomi Ueda, Kengo A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data |
title | A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data |
title_full | A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data |
title_fullStr | A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data |
title_full_unstemmed | A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data |
title_short | A Principal Component Analysis Approach to Estimate the Disability Status for Patients with Multiple Sclerosis Using Japanese Claims Data |
title_sort | principal component analysis approach to estimate the disability status for patients with multiple sclerosis using japanese claims data |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857383/ https://www.ncbi.nlm.nih.gov/pubmed/35064908 http://dx.doi.org/10.1007/s40120-022-00324-0 |
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