Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons' Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, School of Medicine, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University, Shanghai, China.
Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, School of Medicine, Neurotoxin Research CenterTongji HospitalTongji University, Shanghai, China.
Neurol Sci. 2024 Jun;45(6):2661-2670. doi: 10.1007/s10072-023-07296-5. Epub 2024 Jan 6.
The acute levodopa challenge test (ALCT) is an important and valuable examination but there are still some shortcomings with it. We aimed to objectively assess ALCT based on a depth camera and filter out the best indicators.
Fifty-nine individuals with parkinsonism completed ALCT and the improvement rate (IR, which indicates the change in value before and after levodopa administration) of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was calculated. The kinematic features of the patients' movements in both the OFF and ON states were collected with an Azure Kinect depth camera.
The IR of MDS-UPDRS III was significantly correlated with the IRs of many kinematic features for arising from a chair, pronation-supination movements of the hand, finger tapping, toe tapping, leg agility, and gait (r = - 0.277 ~ - 0.672, P < 0.05). Moderate to high discriminative values were found in the selected features in identifying a clinically significant response to levodopa with sensitivity, specificity, and area under the curve (AUC) in the range of 50-100%, 47.22%-97.22%, and 0.673-0.915, respectively. The resulting classifier combining kinematic features of toe tapping showed an excellent performance with an AUC of 0.966 (95% CI = 0.922-1.000, P < 0.001). The optimal cut-off value was 21.24% with sensitivity and specificity of 94.44% and 87.18%, respectively.
This study demonstrated the feasibility of measuring the effect of levodopa and objectively assessing ALCT based on kinematic data derived from an Azure Kinect-based system.
急性左旋多巴挑战试验（ALCT）是一项重要且有价值的检查，但仍存在一些缺点。我们旨在通过深度相机客观评估 ALCT，并筛选出最佳指标。
59 名帕金森病患者完成了 ALCT，并计算了运动障碍协会赞助的帕金森病修订统一评分量表第三部分（MDS-UPDRS III）的改善率（IR，用于表示左旋多巴给药前后的变化）。使用 Azure Kinect 深度相机采集患者在 OFF 和 ON 状态下运动的运动学特征。
MDS-UPDRS III 的 IR 与许多运动学特征的 IR 显著相关，这些特征包括从椅子上站起来、手的旋前-旋后运动、手指敲击、脚趾敲击、腿部敏捷性和步态（r= −0.277~−0.672，P<0.05）。在识别对左旋多巴有临床显著反应的选定特征中，发现具有中等至高的判别值，灵敏度、特异性和曲线下面积（AUC）范围为 50-100%、47.22%-97.22%和 0.673-0.915，分别。结合脚趾敲击运动学特征的分类器表现出优异的性能，AUC 为 0.966（95%CI=0.922-1.000，P<0.001）。最佳截断值为 21.24%，灵敏度和特异性分别为 94.44%和 87.18%。
本研究证明了基于 Azure Kinect 系统获得的运动学数据测量左旋多巴效果和客观评估 ALCT 的可行性。
