Lai Ting-Yu, Chen Hsiao-I, Shih Cho-Chiang, Kuo Li-Chieh, Hsu Hsiu-Yun, Huang Chih-Chung
Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
Department of Occupational Therapy, National Cheng Kung University, Tainan, Taiwan.
Sci Rep. 2016 Aug 5;6:31102. doi: 10.1038/srep31102.
This study aims to determine if the relative displacement between the extensor digitorum communis (EDC) tendon and its surrounding tissues can be used as an adhesion index (AI) for assessing adhesion in metacarpal fractures by comparing two clinical measures, namely single-digit-force and extensor lag (i.e., the difference between passive extension and full active extension). The Fisher-Tippett block-matching method and a Kalman-filter algorithm were used to determine the relative displacements in 39 healthy subjects and 8 patients with metacarpal fractures. A goniometer was used to measure the extensor lag, and a force sensor was used to measure the single-digit-force. Measurements were obtained twice for each patient to evaluate the performance of the AI in assessing the progress of rehabilitation. The Pearson correlation coefficient was calculated to quantify the various correlations between the AI, extensor lag, and single-digit-force. The results showed strong correlations between the AI and the extensor lag, the AI and the single-digit-force, and the extensor lag and the single-digit-force (r = 0.718, -0.849, and -0.741; P = 0.002, P < 0.001, and P = 0.001, respectively). The AI in the patients gradually decreased after continuous rehabilitation, but remained higher than that of healthy participants.
本研究旨在通过比较两种临床测量方法，即单指力和伸肌滞后（即被动伸展与完全主动伸展之间的差异），来确定示指伸肌（EDC）肌腱与其周围组织之间的相对位移是否可作为评估掌骨骨折粘连的粘连指数（AI）。采用Fisher-Tippett块匹配法和卡尔曼滤波算法确定39名健康受试者和8名掌骨骨折患者的相对位移。使用角度计测量伸肌滞后，使用力传感器测量单指力。对每位患者进行两次测量，以评估AI在评估康复进展方面的性能。计算Pearson相关系数以量化AI、伸肌滞后和单指力之间的各种相关性。结果显示AI与伸肌滞后、AI与单指力以及伸肌滞后与单指力之间存在强相关性（r分别为0.718、-0.849和-0.741；P分别为0.002、P<0.001和P=0.001）。患者的AI在持续康复后逐渐降低，但仍高于健康参与者。
