German Center for Neurodegenerative Diseases (DZNE), University of Tübingen, Otfried-Müller-Str. 23, 72076, Tübingen, Germany.
Hertie Institute for Clinical Brain Research, Department of Neurodegenerative Diseases, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
J Neurol. 2018 Sep;265(9):1976-1984. doi: 10.1007/s00415-018-8942-4. Epub 2018 Jun 23.
The early diagnosis of mild cognitive impairment (PD-MCI) in Parkinson's disease (PD) is essential as it increases the future risk for PD dementia (PDD). Recently, a novel weighting algorithm for the Montreal Cognitive Assessment (MoCA) subtests has been reported, to best discriminate between those with and without cognitive impairment in PD. The aim of our study was to validate this scoring algorithm in a large sample of non-demented PD patients, hypothesizing that the weighted MoCA would have a higher diagnostic accuracy for PD-MCI than the original MoCA.
In 202 non-demented PD patients, we evaluated cognitive status, clinical and demographic data, as well as the MoCA with a weighted and unweighted score. Receiver operating characteristic (ROC) curve analysis was used to evaluate discriminative ability of the MoCA. Group comparisons and ROC analysis were performed for PD-MCI classifications with a cut-off ≤ 1, 1.5, and 2 standard deviation (SD) below appropriate norms.
PD-MCI patients scored lower on the weighted than the original MoCA version (p < 0.001) compared to PD patients with normal cognitive function. Areas under the curve only differed significantly for the 2 SD cut-off, leading to an increased sensitivity of the weighted MoCA score (72.9% vs. 70.5%) and specificity compared to the original version (79.0% vs. 65.4%).
Our results indicate better discriminant power for the weighted MoCA compared to the original for more advanced stages of PD-MCI (2 SD cut-off). Future studies are needed to evaluate the predictive value of the weighted MoCA for PDD.
早期诊断帕金森病（PD）患者的轻度认知障碍（PD-MCI）至关重要，因为它会增加 PD 痴呆（PDD）的未来风险。最近，一种蒙特利尔认知评估（MoCA）子测试的新型加权算法已被报道，可最佳区分 PD 患者有无认知障碍。我们的研究旨在在大量非痴呆 PD 患者中验证该评分算法，假设加权 MoCA 对 PD-MCI 的诊断准确性高于原始 MoCA。
在 202 名非痴呆 PD 患者中，我们评估了认知状态、临床和人口统计学数据，以及加权和非加权 MoCA 评分。使用接收者操作特征（ROC）曲线分析评估 MoCA 的区分能力。使用≤1、1.5 和 2 个标准差（SD）低于适当标准的截断值，对 PD-MCI 分类进行组间比较和 ROC 分析。
与认知功能正常的 PD 患者相比，PD-MCI 患者的加权 MoCA 评分低于原始 MoCA 版本（p<0.001）。曲线下面积仅在 2 SD 截断值上有显著差异，导致加权 MoCA 评分的敏感性（72.9%比 70.5%）和特异性（79.0%比 65.4%）较原始版本增加。
与原始 MoCA 相比，加权 MoCA 在更晚期的 PD-MCI（2 SD 截断值）下具有更好的区分能力。未来需要研究评估加权 MoCA 对 PDD 的预测价值。
