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帕金森病认知障碍的筛查:通过子测验加权提高蒙特利尔认知评估量表(MoCA)的诊断效用

Screening for Cognitive Impairment in Parkinson's Disease: Improving the Diagnostic Utility of the MoCA through Subtest Weighting.

作者信息

Fengler Sophie, Kessler Josef, Timmermann Lars, Zapf Alexandra, Elben Saskia, Wojtecki Lars, Tucha Oliver, Kalbe Elke

机构信息

Department of Medical Psychology, Neuropsychology and Gender Studies, University Hospital Cologne, Cologne, Germany.

Institute of Gerontology & Center for Neuropsychological Diagnostics and Intervention (CeNDI), University of Vechta, Vechta, Germany.

出版信息

PLoS One. 2016 Jul 20;11(7):e0159318. doi: 10.1371/journal.pone.0159318. eCollection 2016.

Abstract

BACKGROUND

Given the high prevalence of cognitive impairment in Parkinson's disease (PD), cognitive screening is important in clinical practice. The Montreal Cognitive Assessment (MoCA) is a frequently used screening test in PD to detect mild cognitive impairment (PD-MCI) and Parkinson's disease dementia (PD-D). However, the proportion in which the subtests are represented in the MoCA total score does not seem reasonable. We present the development and preliminary evaluation of an empirically based alternative scoring system of the MoCA which aims at increasing the overall diagnostic accuracy.

METHODS

In study 1, the MoCA was administered to 40 patients with PD without cognitive impairment (PD-N), PD-MCI, or PD-D, as defined by a comprehensive neuropsychological test battery. The new MoCA scoring algorithm was developed by defining Areas under the Curve (AUC) for MoCA subtests in a Receiver Operating Characteristic (ROC) and by weighting the subtests according to their sensitivities and specificities. In study 2, an independent sample of 24 PD patients (PD-N, PD-MCI, or PD-D) was tested with the MoCA. In both studies, diagnostic accuracy of the original and the new scoring procedure was calculated.

RESULTS

Diagnostic accuracy increased with the new MoCA scoring algorithm. In study 1, the sensitivity to detect cognitive impairment increased from 62.5% to 92%, while specificity decreased only slightly from 77.7% to 73%; in study 2, sensitivity increased from 68.8% to 81.3%, while specificity stayed stable at 75%.

CONCLUSION

This pilot study demonstrates that the sensitivity of the MoCA can be enhanced substantially by an empirically based weighting procedure and that the proposed scoring algorithm may serve the MoCA's actual purpose as a screening tool in the detection of cognitive dysfunction in PD patients better than the original scoring of the MoCA. Further research with larger sample sizes is necessary to establish efficacy of the alternate scoring system.

摘要

背景

鉴于帕金森病(PD)中认知障碍的高患病率,认知筛查在临床实践中很重要。蒙特利尔认知评估量表(MoCA)是PD中常用的筛查测试,用于检测轻度认知障碍(PD-MCI)和帕金森病痴呆(PD-D)。然而,MoCA总分中各子测试所占比例似乎不合理。我们介绍了一种基于经验的MoCA替代评分系统的开发和初步评估,其目的是提高总体诊断准确性。

方法

在研究1中,对40例无认知障碍的PD患者(PD-N)、PD-MCI或PD-D患者进行了MoCA测试,这些患者由一套全面的神经心理学测试组合定义。通过在受试者工作特征(ROC)曲线中定义MoCA子测试的曲线下面积(AUC),并根据其敏感性和特异性对各子测试进行加权,开发了新的MoCA评分算法。在研究2中,对24例PD患者(PD-N、PD-MCI或PD-D)的独立样本进行了MoCA测试。在两项研究中,均计算了原始评分程序和新评分程序的诊断准确性。

结果

新的MoCA评分算法提高了诊断准确性。在研究1中,检测认知障碍的敏感性从62.5%提高到92%,而特异性仅从77.7%略微下降到73%;在研究2中,敏感性从68.8%提高到81.3%,而特异性稳定在75%。

结论

这项初步研究表明,通过基于经验的加权程序可以大幅提高MoCA的敏感性,并且所提出的评分算法可能比MoCA的原始评分更好地服务于MoCA作为筛查工具在检测PD患者认知功能障碍方面的实际目的。需要更大样本量的进一步研究来确定替代评分系统的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fcf/4954721/23318a254e05/pone.0159318.g001.jpg

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