RADAR-AD:评估用于早期检测阿尔茨海默病的多种远程监控技术。

IF 7.9 1区 医学 Q1 CLINICAL NEUROLOGY
Manuel Lentzen, Srinivasan Vairavan, Marijn Muurling, Vasilis Alepopoulos, Alankar Atreya, Merce Boada, Casper de Boer, Pauline Conde, Jelena Curcic, Giovanni Frisoni, Samantha Galluzzi, Martha Therese Gjestsen, Mara Gkioka, Margarita Grammatikopoulou, Lucrezia Hausner, Chris Hinds, Ioulietta Lazarou, Alexandre de Mendonça, Spiros Nikolopoulos, Dorota Religa, Gaetano Scebba, Pieter Jelle Visser, Gayle Wittenberg, Vaibhav A Narayan, Neva Coello, Anna-Katharine Brem, Dag Aarsland, Holger Fröhlich
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引用次数: 0

摘要

背景:阿尔茨海默病(AD)是一种进行性神经退行性疾病,影响全球数百万人,导致认知和功能下降。早期发现和干预对于提高患者及其家属的生活质量至关重要。远程监控技术(rmt)通过跟踪行为和认知功能(如记忆、语言和解决问题的技能)的变化,为早期检测提供了一个很有前途的解决方案。及时发现这些症状可促进早期干预,从而有可能减缓疾病进展并实现适当的治疗和护理。方法:RADAR-AD研究旨在评估多种rmt在真实世界中检测AD不同阶段功能衰退的准确性和有效性,并与标准临床评定量表进行比较。我们的方法包括单变量分析,使用协方差分析(ANCOVA)来分析六个rmt的个体特征,同时调整诸如年龄、性别、受教育年限、临床地点、BMI和季节等变量。此外,我们采用了四种机器学习分类器——逻辑回归、决策树、随机森林和XGBoost——使用嵌套交叉验证方法来评估rmt的区分能力。结果:ANCOVA结果显示,健康受试者和AD受试者在体力活动减少、快速眼动睡眠减少、步态模式改变和认知功能下降方面存在显著差异。基于机器学习的分析表明,基于rmt的模型可以识别出处于前驱期的受试者,ROC曲线下面积为73.0%。此外,我们的研究结果表明,阿姆斯特丹iADL问卷具有较高的歧视能力。结论:RMTs在已经处于前驱期的AD检测中显示出希望。使用它们可以使早期发现和干预成为可能,从而提高患者的生活质量。此外,阿姆斯特丹iADL问卷在远程使用时具有很高的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer's disease.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills. Timely detection of these symptoms can facilitate early intervention, potentially slowing disease progression and enabling appropriate treatment and care.

Methods: The RADAR-AD study was designed to evaluate the accuracy and validity of multiple RMTs in detecting functional decline across various stages of AD in a real-world setting, compared to standard clinical rating scales. Our approach involved a univariate analysis using Analysis of Covariance (ANCOVA) to analyze individual features of six RMTs while adjusting for variables such as age, sex, years of education, clinical site, BMI and season. Additionally, we employed four machine learning classifiers - Logistic Regression, Decision Tree, Random Forest, and XGBoost - using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs.

Results: The ANCOVA results indicated significant differences between healthy and AD subjects regarding reduced physical activity, less REM sleep, altered gait patterns, and decreased cognitive functioning. The machine-learning-based analysis demonstrated that RMT-based models could identify subjects in the prodromal stage with an Area Under the ROC Curve of 73.0 %. In addition, our findings show that the Amsterdam iADL questionnaire has high discriminatory abilities.

Conclusions: RMTs show promise in AD detection already in the prodromal stage. Using them could allow for earlier detection and intervention, thereby improving patients' quality of life. Furthermore, the Amsterdam iADL questionnaire holds high potential when employed remotely.

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来源期刊
Alzheimer's Research & Therapy
Alzheimer's Research & Therapy 医学-神经病学
CiteScore
13.10
自引率
3.30%
发文量
172
审稿时长
>12 weeks
期刊介绍: Alzheimer's Research & Therapy is an international peer-reviewed journal that focuses on translational research into Alzheimer's disease and other neurodegenerative diseases. It publishes open-access basic research, clinical trials, drug discovery and development studies, and epidemiologic studies. The journal also includes reviews, viewpoints, commentaries, debates, and reports. All articles published in Alzheimer's Research & Therapy are included in several reputable databases such as CAS, Current contents, DOAJ, Embase, Journal Citation Reports/Science Edition, MEDLINE, PubMed, PubMed Central, Science Citation Index Expanded (Web of Science) and Scopus.
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