Digital biomarkers: Redefining clinical outcomes and the concept of meaningful change

IF 6.8 Q1 CLINICAL NEUROLOGY

Alzheimer''s and Dementia: Translational Research and Clinical Interventions Pub Date : 2025-06-02 DOI:10.1002/trc2.70114

Maria Florencia Iulita, Emmanuel Streel, John Harrison

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引用次数: 0

Abstract

MCID (minimal clinically important difference) is a patient-centered concept used in clinical research that represents the smallest change that someone living with Alzheimer's disease would identify as important. There are several challenges associated with the universal application of this construct. Alzheimer's disease progresses differently for each individual, complicating the establishment of a universal standard that accounts for individual-level issues. Alzheimer's disease is also a gradual and evolving disorder, and what is perceived as clinically meaningful can vary significantly at early and late disease stages. People living with Alzheimer's disease and caregivers may have differing perspectives on the benefits of treatment outcomes, making it more challenging to establish an appropriate MCID. Moreover, Alzheimer's trials rely on a variety of tests to evaluate cognitive and functional impairments. However, these tests often lack sensitivity to early-stage changes and are affected by variability in rater rankings. Digital biomarkers and advanced health technologies have emerged as a hot topic in modern medicine. They offer a promising approach for detecting real-time, objective clinical differences and improving patient outcomes by enabling continuous monitoring, individualized assessments, and leveraging artificial intelligence learning for complex analytical predictions. However, while these advancements hold great potential, they also raise important considerations around standardization, accuracy, and integration into current clinical frameworks. As new technologies are introduced alongside evolving regulatory frameworks, the primary focus must remain on outcomes that truly matter to people living with Alzheimer's disease and their caregivers, ensuring that the principle of clinical meaningfulness is not lost.

Highlights

Minimal clinically important difference (MCID) represents the smallest change in a patient's condition that would be considered meaningful, but defining this for Alzheimer's disease is challenging due to its heterogeneity.
The perception of what is clinically meaningful may differ at the individual level, at different disease stages within the same individual, and between patient and caregiver.
Traditional tests used as endpoints in Alzheimer's trials lack the sensitivity to detect subtle changes and are limited by range restrictions, making them less effective for accurately capturing treatment efficacy.
Digital biomarkers and artificial intelligence (AI)-driven health technologies may offer the potential to enhance the detection of clinically meaningful changes by providing continuous, objective monitoring and advanced analytics for individualized patient assessments.
Both the United States Food and Drug Administration (FDA) and European Medicines Agency (EMA) are playing pivotal roles in advancing the use of digital health technologies, facilitating the evolution of regulatory frameworks to ensure these innovations are effectively integrated into clinical research and practice.

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数字生物标志物：重新定义临床结果和有意义改变的概念

MCID（最小临床重要差异）是一个以患者为中心的概念，用于临床研究，代表阿尔茨海默病患者认为重要的最小变化。这个结构的普遍应用存在一些挑战。阿尔茨海默病的进展因人而异，这使得建立一个能解释个人层面问题的通用标准变得复杂。阿尔茨海默病也是一种逐渐发展的疾病，在疾病的早期和晚期，临床意义可能会有很大差异。阿尔茨海默病患者和护理人员可能对治疗结果的益处有不同的看法，这使得建立适当的MCID更具挑战性。此外，阿尔茨海默氏症的试验依赖于各种测试来评估认知和功能障碍。然而，这些测试往往缺乏对早期变化的敏感性，并受到评级变化的影响。数字生物标志物和先进的健康技术已成为现代医学的热门话题。它们提供了一种很有前途的方法，可以检测实时、客观的临床差异，并通过实现持续监测、个性化评估和利用人工智能学习进行复杂的分析预测来改善患者的预后。然而，尽管这些进步具有巨大的潜力，但它们也引起了关于标准化、准确性和与当前临床框架整合的重要考虑。随着新技术的引入以及不断发展的监管框架，首要重点必须仍然是对阿尔茨海默病患者及其护理人员真正重要的结果，确保临床意义的原则不会丢失。最小临床重要差异（minimum clinical important difference， MCID）代表患者状况中被认为有意义的最小变化，但由于阿尔茨海默病的异质性，对其进行定义具有挑战性。在个体层面上，在同一个体的不同疾病阶段，以及在患者和护理者之间，对什么是临床意义的感知可能会有所不同。在阿尔茨海默氏症试验中用作终点的传统测试缺乏检测细微变化的灵敏度，并且受到范围限制的限制，使得它们在准确捕捉治疗效果方面效果较差。数字生物标志物和人工智能（AI）驱动的卫生技术可能会通过为个性化患者评估提供持续、客观的监测和高级分析，从而增强对临床有意义变化的检测。美国食品和药物管理局（FDA）和欧洲药品管理局（EMA）在推进数字医疗技术的使用、促进监管框架的演变以确保这些创新有效地融入临床研究和实践方面发挥着关键作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer''s and Dementia: Translational Research and Clinical Interventions Medicine-Psychiatry and Mental Health

CiteScore

10.10

自引率

2.10%

发文量

134

审稿时长

10 weeks

期刊介绍： Alzheimer''s & Dementia: Translational Research & Clinical Interventions (TRCI) is a peer-reviewed, open access,journal from the Alzheimer''s Association®. The journal seeks to bridge the full scope of explorations between basic research on drug discovery and clinical studies, validating putative therapies for aging-related chronic brain conditions that affect cognition, motor functions, and other behavioral or clinical symptoms associated with all forms dementia and Alzheimer''s disease. The journal will publish findings from diverse domains of research and disciplines to accelerate the conversion of abstract facts into practical knowledge: specifically, to translate what is learned at the bench into bedside applications. The journal seeks to publish articles that go beyond a singular emphasis on either basic drug discovery research or clinical research. Rather, an important theme of articles will be the linkages between and among the various discrete steps in the complex continuum of therapy development. For rapid communication among a multidisciplinary research audience involving the range of therapeutic interventions, TRCI will consider only original contributions that include feature length research articles, systematic reviews, meta-analyses, brief reports, narrative reviews, commentaries, letters, perspectives, and research news that would advance wide range of interventions to ameliorate symptoms or alter the progression of chronic neurocognitive disorders such as dementia and Alzheimer''s disease. The journal will publish on topics related to medicine, geriatrics, neuroscience, neurophysiology, neurology, psychiatry, clinical psychology, bioinformatics, pharmaco-genetics, regulatory issues, health economics, pharmacoeconomics, and public health policy as these apply to preclinical and clinical research on therapeutics.