Background: Writer's cramp (WC) dystonia presents with abnormal postures during the task of writing and is an ideal dystonia subtype to study disease mechanisms for all forms of focal dystonia. Development of novel therapies is contingent on identifying sensitive and specific measures that can relate to the clinical syndrome and achieve a realistic sample size to power clinical research study for a rare disease. Although there have been prior studies employing automated measures of writing kinematics, it remains unclear which measures can distinguish WC subjects with high sensitivity and specificity and how these measures relate to clinician rating scales and patient-reported disability. The goal of this study was to: 1-identify automated writing measures that distinguish WC from healthy subjects, 2-measure sensitivity and specificity of these automated measures as well as their association with established dystonia rating scales, and 3-determine the sample size needed for each automated measure to power a clinical research study. Methods: 21 WC dystonia and 22 healthy subjects performed a sentence-copying assessment on a digital tablet in a kinematic software and hand recognition software. The sensitivity and specificity of automated measures was calculated using a logistic regression model. Measures were then correlated with examiner and patient rating scales. Power analysis was performed for 2 clinical research designs using these automated measures. Results: Of the 23 automated writing measures analyzed, only 3 measures showed promise for use in a clinical research study. The automated measures of writing legibility, duration, and peak acceleration were able to distinguish WC from healthy controls with high sensitivity and specificity, correlated with examiner-rated dystonia sub-score measures and demonstrated relatively smaller sample sizes suitable for research studies in a rare disease population. Discussion: We identified novel automated writing outcome measures for use in clinical research studies of WC subjects which capture key aspects of the clinical disease and can serve as important readout of dystonia disease mechanism as well as future disease interventions.

中文翻译：

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设计作家抽筋肌张力障碍的研究：自动书写方式的分析

背景：作家的抽筋（WC）肌张力障碍在写作过程中表现出异常的姿势，是研究各种形式的局灶性肌张力障碍的疾病机制的理想肌张力障碍亚型。新疗法的开发取决于确定与临床综合征相关的敏感和具体措施，并获得现实的样本量，从而为罕见疾病的临床研究提供动力。尽管先前有研究采用自动运动学方法进行运动学研究，但尚不清楚哪种方法能以较高的敏感性和特异性来区分WC受试者，以及这些方法与临床医师评定量表和患者报告的残疾之间的关系。这项研究的目的是：1-确定能够将WC与健康受试者区分开的自动书写方式，2项措施对这些自动化措施的敏感性和特异性，以及它们与已建立的肌张力障碍评定量表的关联，以及3确定每种自动化措施为临床研究提供动力所需的样本量。方法：21位WC肌张力障碍患者和22位健康受试者通过运动学软件和手部识别软件在数字平板电脑上进行了句子复制评估。使用logistic回归模型计算自动化措施的敏感性和特异性。然后将量度与检查者和患者的评分量表相关联。使用这些自动化措施对2个临床研究设计进行了功率分析。结果：在分析的23种自动书写工具中，只有3种工具显示有望用于临床研究。自动化的书写易读性，持续时间，峰值加速度和峰值加速度能够以较高的灵敏度和特异性将WC与健康对照区分开来，并与检查者评定的肌张力障碍亚评分相关，并证明相对较小的样本量适用于罕见疾病人群的研究。讨论：我们确定了用于WC受试者的临床研究中的新颖的自动写作结局指标，这些指标可以捕获临床疾病的关键方面，并且可以作为肌张力障碍疾病机制以及未来疾病干预措施的重要信息。