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基于智能手机的患者对神经病变等级的自我评估:对神经病变追踪器与临床试验对齐的验证研究

Astrid Emilie Kongsmark Madsen¹, Maria Joy Normann Haverberg¹, Jakob Eyvind Bardram²

¹Department of Neurology, Zealand University Hospital Roskilde, Roskilde, Denmark.

BMJ neurology open

|November 5, 2025

概括

一个智能手机应用程序允许患有远距离对称外围神经病变 (DSPN) 的患者自我调整症状和迹象. 这种数字工具显示出对神经病变的优异分类能力,有助于在没有诊所访问的情况下进行临床评估.

科学领域:

神经学神经学
数字健康数字健康
医疗技术医疗技术医学技术

背景情况:

远距离对称外围神经病变 (DSPN) 需要频繁的症状和信号分级,给患者和医疗保健带来负担.
目前的评估方法需要面对面的访问,这对持续的患者监测带来了挑战.

研究的目的:

开发和验证基于智能手机的应用程序,神经病变追踪器,用于患者的自我记录和DSPN的检查.
评估患者管理的分级与专业临床评估的对齐性和分类准确性.

主要方法:

21名DSPN患者和18名对照参与了一项试验复试研究.
参与者使用神经病变追踪器进行自我评估,并使用犹他州早期神经病变量表 (UENS) 和总神经病变评分 - 临床版本 (TNSc) 进行临床分级.
定量测量包括温度值和神经导电研究.

主要成果:

布兰德-阿尔特曼分析显示神经病变追踪器和UENS之间的可比性信任极限相似,TNSc显示较窄的极限.
在神经病变追踪器和UENS之间的测试分数差异中没有观察到显著的偏差.
接收器操作曲线分析表明神经病变追踪器的优异分类能力 (AUC 0.903±0.053),略低于UENS和TNSc.

结论:

通过神经病变追踪器对患者进行的神经病变分级,可以补充DSPN的临床评估.
该应用程序为远程监控提供了一个潜在的解决方案,并减少了对门诊诊所访问的需求.

关键词:

神经病变是一种神经病变.

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