Case Control Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatry. Aug 19, 2022; 12(8): 1031-1043
Published online Aug 19, 2022. doi: 10.5498/wjp.v12.i8.1031
Can the prediction model using regression with optimal scale improve the power to predict the Parkinson's dementia?
Haewon Byeon
Haewon Byeon, Department of Medical Big Data, Inje University, Gimhae 50834, South Korea
Author contributions: Byeon H was designed the study, involved in data interpretation, preformed the statistical analysis, and assisted with writing the article.
Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, No. NRF-2018R1D1A1B07041091 and No. NRF-2021S1A5A8062526; and 2022 Development of Open-Lab based on 4P in the Southeast Zone.
Institutional review board statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of National Biobank of Korea under Korea Centers for Disease Control and Prevention (protocol code KBN-2019-1327).
Informed consent statement: All patients gave informed consent.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code from the corresponding author at bhwpuma@naver.com.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Haewon Byeon, DSc, PhD, Associate Professor, Director, Department of Medical Big Data, Inje University, No. 329, Gimhae 50834, South Korea. bhwpuma@naver.com
Received: March 16, 2022
Peer-review started: March 16, 2022
First decision: June 11, 2022
Revised: June 15, 2022
Accepted: July 11, 2022
Article in press: July 11, 2022
Published online: August 19, 2022
Processing time: 154 Days and 19.9 Hours
Abstract
BACKGROUND

Efficiently detecting Parkinson's disease (PD) with dementia (PDD) as soon as possible is an important issue in geriatric medicine.

AIM

To develop a model for predicting PDD based on various neuropsychological tests using data from a nationwide survey conducted by the Korean Centers for Disease Control and Prevention and to present baseline data for the early detection of PDD.

METHODS

This study comprised 289 patients who were 60 years or older with PD [110 with PDD and 179 Parkinson's Disease-Mild Cognitive Impairment (PD-MCI)]. Regre-ssion with optimal scaling (ROS) was used to identify independent relationships between the neuropsychological test results and PDD.

RESULTS

In the ROS analysis, Korean version of mini mental state ex-amination (MMSE) (KOREAN version of MMSE) (b = -0.52, SE = 0.16) and Hoehn and Yahr staging (b = 0.44, SE = 0.19) were significantly effective models for distinguishing PDD from PD-MCI (P < 0.05), even after adjusting for all of the Parkinson's motor symptom and neuropsychological test results. The optimal number of categories (scaling factors) for KOREAN version of MMSE and Hoehn and Yahr Scale was 10 and 7, respectively.

CONCLUSION

The results of this study suggest that among the various neuropsychological tests conducted, the optimal classification scores for KOREAN version of MMSE and Hoehn and Yahr Scale could be utilized as an effective screening test for the early discrimination of PDD from PD-MCI.

Keywords: Hoehn and Yahr staging; Optimal scale; Parkinson's dementia; Mini mental state ex-amination; Montreal Cognitive Assessment

Core Tip: Although a general linear model can be constructed if all of the variables used in the analysis are numeric, it is difficult to fit the data when the variables are nominal. We build a regression model using the transform variables obtained by iteratively using alternating least squares to compute the optimal scaling. We developed a predictive model to discriminate Parkinson's disease with dementia from Parkinson's Disease-Mild Cognitive Impairment based on the results of nine neuropsychological tests and found that only Korean version of mini mental state examination and Hoehn and Yahr Scale could be successfully employed to this end.