Department of Neurology, Mayo Clinic, 200 First Street SW, Rochester, Minnesota, USA, 55905.
Muscle Nerve. 2014 May;49(5):645-53. doi: 10.1002/mus.23982. Epub 2014 Jan 28.
We assessed proficiency (accuracy and intra- and intertest reproducibility) of smart quantitative sensation tests (smart QSTs) in subjects without and with diabetic sensorimotor polyneuropathy (DSPN).
Technologists from 3 medical centers using different but identical QSTs independently assessed 6 modalities of sensation of the foot (or leg) twice in patients without (n = 6) and with (n = 6) DSPN using smart computer assisted QSTs.
Low rates of test abnormalities were observed in health and high rates in DSPN. Very high intraclass correlations were obtained between continuous measures of QSTs and neuropathy signs, symptoms, or nerve conductions (NCs). No significant intra- or intertest differences were observed.
These results provide proof of concept that smart QSTs provide accurate assessment of sensation loss without intra- or intertest differences useful for multicenter trials. Smart technology makes possible efficient testing of body surface area sensation loss in symmetric length-dependent sensorimotor polyneuropathies.
我们评估了智能定量感觉测试(smart QST)在无和有糖尿病感觉运动多发性神经病(DSPN)的受试者中的熟练程度(准确性以及组内和组间的可重复性)。
来自 3 个医疗中心的技术人员使用不同但完全相同的 QST 分别对无(n=6)和有(n=6)DSPN 的患者足部(或腿部)的 6 种感觉进行两次独立的智能计算机辅助 QST 评估。
在健康人群中观察到的测试异常率较低,而在 DSPN 患者中则较高。QST 的连续测量值与神经病变的体征、症状或神经传导(NC)之间存在非常高的组内相关性。未观察到组内或组间差异有统计学意义。
这些结果提供了一个概念证明,即智能 QST 可准确评估感觉丧失,且不存在组内或组间差异,非常适用于多中心试验。智能技术使得对称长度依赖性感觉运动多发性神经病的体表感觉丧失的高效测试成为可能。