Tremor stability index: a new tool for differential diagnosis in tremor syndromes.
Tremor stability index: a new tool for differential diagnosis in tremor syndromes.
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DOI：
10.1093/brain/awx104
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发表时间：
2017-07-01
期刊：
影响因子：
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通讯作者：
Brown P
中科院分区：
文献类型：
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作者：
di Biase L;Brittain JS;Shah SA;Pedrosa DJ;Cagnan H;Mathy A;Chen CC;Martín-Rodríguez JF;Mir P;Timmerman L;Schwingenschuh P;Bhatia K;Di Lazzaro V;Brown P
震颤综合征的误诊很常见，并且会影响临床治疗和研究。到目前为止，还没有经过验证的神经生理学技术被证明具有良好的分类性能，诊断的金标准是运动障碍专家进行的临床评估。我们提出了一种稳健的新型神经生理学指标——震颤稳定性指数，它能够以较高的诊断准确性区分帕金森病震颤和特发性震颤。震颤稳定性指数源自对震颤活动的运动学测量。在一个测试队列中对其进行了评估，该队列包括16例以震颤为主的帕金森病患者的静止震颤记录和20例特发性震颤患者的姿势性震颤记录，并在第二个独立队列中进行了验证，该队列包括另外50例帕金森病和特发性震颤患者的震颤记录。以临床诊断作为金标准。在每位患者中选择100秒的震颤记录进行分析。通过二元逻辑回归和受试者工作特征分析评估新指标的分类准确性。通过计算敏感性、特异性、准确性、阳性似然比、阴性似然比、受试者工作特征曲线下面积以及交叉验证来检验诊断性能。在测试数据集和验证数据集中，截断值为1.05的震颤稳定性指数对帕金森病震颤和特发性震颤都有良好的分类性能。震颤稳定性指数的最大敏感性、特异性和准确性分别为95%、95%和92%。受试者工作特征分析显示，测试数据集的曲线下面积为0.916（95%置信区间0.797 - 1.000），验证数据集的值为0.855（95%置信区间0.754 - 0.957）。分类准确性证明与记录设备和姿势无关。震颤稳定性指数有助于两种最常见震颤类型的鉴别诊断。它具有较高的诊断准确性，可以从简短、廉价、广泛可用且无创的震颤记录中得出，并且在解读时不受操作人员或姿势背景的影响。
Misdiagnosis among tremor syndromes is common, and can impact on both clinical care and research. To date no validated neurophysiological technique is available that has proven to have good classification performance, and the diagnostic gold standard is the clinical evaluation made by a movement disorders expert. We present a robust new neurophysiological measure, the tremor stability index, which can discriminate Parkinson’s disease tremor and essential tremor with high diagnostic accuracy. The tremor stability index is derived from kinematic measurements of tremulous activity. It was assessed in a test cohort comprising 16 rest tremor recordings in tremor-dominant Parkinson’s disease and 20 postural tremor recordings in essential tremor, and validated on a second, independent cohort comprising a further 50 tremulous Parkinson’s disease and essential tremor recordings. Clinical diagnosis was used as gold standard. One hundred seconds of tremor recording were selected for analysis in each patient. The classification accuracy of the new index was assessed by binary logistic regression and by receiver operating characteristic analysis. The diagnostic performance was examined by calculating the sensitivity, specificity, accuracy, likelihood ratio positive, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-validation. Tremor stability index with a cut-off of 1.05 gave good classification performance for Parkinson’s disease tremor and essential tremor, in both test and validation datasets. Tremor stability index maximum sensitivity, specificity and accuracy were 95%, 95% and 92%, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.916 (95% confidence interval 0.797–1.000) for the test dataset and a value of 0.855 (95% confidence interval 0.754–0.957) for the validation dataset. Classification accuracy proved independent of recording device and posture. The tremor stability index can aid in the differential diagnosis of the two most common tremor types. It has a high diagnostic accuracy, can be derived from short, cheap, widely available and non-invasive tremor recordings, and is independent of operator or postural context in its interpretation.