Treatment Detection and Movement Disorder Society‐Unified Parkinson's Disease Rating Scale, Part III Estimation Using Finger Tapping Tasks

Author:

ZhuParris Ahnjili123ORCID,Thijssen Eva12ORCID,Elzinga Willem O.1ORCID,Makai‐Bölöni Soma12ORCID,Kraaij Wessel3ORCID,Groeneveld Geert J.12ORCID,Doll Robert J.1ORCID

Affiliation:

1. Centre for Human Drug Research (CHDR) Leiden The Netherlands

2. Leiden University Medical Centre (LUMC) Leiden The Netherlands

3. Leiden Institute of Advanced Computer Science (LIACS) Leiden The Netherlands

Abstract

AbstractThe validation of objective and easy‐to‐implement biomarkers that can monitor the effects of fast‐acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo‐controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb–index finger tapping (TIFT) tasks and the Movement Disorder Society‐Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS‐UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS‐UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS‐UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS‐UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS‐UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Funder

Centre for Human Drug Research

Publisher

Wiley

Subject

Neurology (clinical),Neurology

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3