A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
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DOI：
10.1038/s41746-020-0286-7
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发表时间：
2020-05-29
影响因子：
15.2
通讯作者：
Lambercy, Olivier
中科院分区：
文献类型：
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作者：
Kanzler, Christoph M.;Rinderknecht, Mike D.;Lambercy, Olivier
数字健康指标有望促进对身体功能受损的理解，例如神经系统疾病。然而，它们的临床整合受到许多现有的、通常是抽象的指标验证不足的挑战。在这里，我们提出了一个数据驱动的框架来选择和验证从技术辅助评估中提取的临床相关的数字健康指标核心集。作为示例性用例，该框架应用于虚拟钉插入测试（VPIT），这是一种技术辅助上肢感觉运动障碍评估。该框架建立在用例特定的病理生理动机的指标，模型人口统计学混淆，并评估最重要的临床计量属性（判别效度，结构效度，可靠性，测量误差，学习效果）。应用于从120个神经完整个体和89个受影响个体收集的77个VPIT指标，该框架允许选择10个临床相关的核心指标。这些以有效、可靠和信息丰富的方式评估了多重感觉运动障碍的严重程度。这些指标提供了额外的临床价值，通过检测在传统量表中没有显示任何缺陷的神经学受试者的损伤，以及通过单一评估覆盖手臂和手的感觉运动损伤。建议的框架提供了一个透明的，逐步选择程序基于临床相关证据。这为优化数学损失函数的既定选择算法创造了一个有趣的替代方案，并且并不总是直观地回溯。这可能有助于解决数字健康指标临床整合不足的问题。对于VPIT，它允许建立有效的核心指标，为将其整合到神经康复试验中铺平道路。
Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.