Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, New York, USA.
Statistical Planning and Analysis Section, Department of Neurology, University of Texas Southwestern, Dallas, Texas, USA.
Ann Clin Transl Neurol. 2024 Jun;11(6):1514-1525. doi: 10.1002/acn3.52068. Epub 2024 Apr 22.
Essential tremor is among the most prevalent neurological diseases. Diagnosis is based entirely on neurological evaluation. Historically, there were few postmortem brain studies, hindering attempts to develop pathologically based criteria to distinguish essential tremor from control brains. However, an intensive effort to bank essential tremor brains over recent years has resulted in postmortem studies involving >200 brains, which have identified numerous degenerative changes in the essential tremor cerebellar cortex. Although essential tremor and controls have been compared with respect to individual metrics of pathology, there has been no overarching analysis to derive a combination of metrics to distinguish essential tremor from controls. We asked whether there is a constellation of pathological findings that separates essential tremor from controls, and how well that constellation performs.
Analyses included 100 essential tremor brains from the essential tremor centralized brain repository and 50 control brains. A standard tissue block from the cerebellar cortex was used to quantify 11 metrics of pathological change. Three supervised classification algorithms were investigated, with data divided into training and validation samples.
Using three different algorithms, we illustrate the ability to correctly predict a diagnosis of essential tremor, with sensitivity and specificity >87%, and in the majority of situations, >90%. We also provide a web-based application that uses these metric values, and based on specified cutoffs, determines the likely diagnosis.
These analyses set the stage for use of pathologically based criteria to distinguish clinically diagnosed essential tremor cases from controls, at the time of postmortem.
特发性震颤是最常见的神经疾病之一。诊断完全基于神经学评估。历史上，对死后大脑的研究很少，这阻碍了制定基于病理学的标准来区分特发性震颤与正常大脑的尝试。然而，近年来，人们积极努力储存特发性震颤的大脑，已经进行了涉及>200 个大脑的尸检研究，这些研究确定了特发性震颤小脑皮质中存在许多退行性变化。尽管已经比较了特发性震颤和对照组在病理学指标方面的个体差异，但尚未进行全面的分析，以得出区分特发性震颤与对照组的指标组合。我们想知道是否存在一组病理学发现可以将特发性震颤与对照组区分开来，以及这种组合的区分效果如何。
分析包括来自特发性震颤集中脑库的 100 例特发性震颤大脑和 50 例对照组大脑。使用标准的小脑皮质组织块来量化 11 项病理学变化指标。研究了三种有监督的分类算法，将数据分为训练和验证样本。
使用三种不同的算法，我们说明了正确预测特发性震颤诊断的能力，敏感性和特异性>87%，在大多数情况下，>90%。我们还提供了一个基于网络的应用程序，该应用程序使用这些度量值，并根据指定的截止值确定可能的诊断。
这些分析为在死后使用基于病理学的标准来区分临床诊断的特发性震颤病例与对照组奠定了基础。
