UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA.
Ann Clin Transl Neurol. 2021 Jan;8(1):4-14. doi: 10.1002/acn3.51187. Epub 2020 Nov 19.
To determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data.
Patients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within-patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics' discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation.
Participants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow-up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow-up, several had evidence of progression by the multisensor metrics.
Within a short follow-up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases.
利用手指和脚踏传感器数据，确定一种小型可穿戴多传感器设备是否能够区分进展型和复发型多发性硬化症（MS），并在短时间内捕捉肢体进展情况。
前瞻性随访 MS 患者，在常规临床就诊期间，大约每 6 个月随访一次。每次就诊时，参与者均穿戴 MYO 腕带进行手指和脚踏传感器数据采集，该腕带包括加速度计、陀螺仪和表面肌电图传感器。通过随时间变化的信号波形特征变化来创建患者内肢体进展的指标。利用计算 AUROC 评估上肢（UE）和下肢（LE）指标区分进展型和复发型 MS 的能力。使用 Pearson 相关性比较与扩展残疾状况量表（EDSS）评分的相关性。
参与者包括 53 例复发型和 15 例进展型 MS（72%为女性，基线平均年龄为 48 岁，中位病程为 11 年，中位 EDSS 为 2.5，中位随访时间为 10 个月）。最终的综合指标通过 UE 的 AUROC 为 0.93 和 LE 的 AUROC 为 0.96 区分复发型和继发性进展型 MS。这些指标与基线 EDSS 相关（UE P=0.0003，LE P=0.0007）。尽管大多数患者在短期随访期间 EDSS 无变化，但部分患者通过多传感器指标显示出进展迹象。
在短期随访期间，这种新型多传感器算法可以区分进展型和复发型 MS，并捕捉肢体功能的变化。这种新颖的方法具有成本低、非侵入性和易于使用的特点，非常适合在临床实践和临床试验中作为 MS 的生命体征。这种方法还有望监测其他神经疾病中的肢体功能障碍。
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