Department of Neurology, "Santa Chiara Hospital", Azienda Provinciale per I Servizi Sanitari (APSS), 38122, Trento, Italy; Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy.
Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy.
Parkinsonism Relat Disord. 2024 Jul;124:106987. doi: 10.1016/j.parkreldis.2024.106987. Epub 2024 Apr 30.
Mild cognitive impairment in Parkinson's disease (PD-MCI) includes deficits in different cognitive domains, and one domain to explore for neurocognitive impairment following the DSM-V is social cognition. However, this domain is not included in current criteria for PD-MCI diagnosis. Moreover, tests vary across studies. It is, therefore, crucial to optimize cognitive assessment in PD-MCI. We aimed to do so by using Machine Learning.
275 PD patients were included. Four cognitive batteries were created: two Standard ones (Levels I and II), applying current criteria and "traditional" tests; two Alternative ones (Levels I and II), which incorporated a test of social cognition. These batteries were included in the Random Forest (RF) classifier. To assess RF performance, the AUC was considered, and the Variable Importance Index was estimated to understand the contribution of each test in PD-MCI classification.
Standard Level I and II showed an AUC of 0.852 and 0.892, while Alternative Level I and II showed an AUC of 0.898 and of 0.906. Variable Importance Index revealed that TMT B-A, Ekman test, RAVLT-IR, MoCA, and Action Naming were tests that most contributed to PD-MCI classification.
The Alternative level I assessment demonstrated a similar classification capacity to the Standard level II assessment. This finding suggests that in the cognitive assessment of PD patients, it is crucial to consider the most affected cognitive domains in this clinical population, including social cognition. Taken together, these results suggest to revise current criteria for the diagnosis of PD-MCI.
帕金森病患者的轻度认知障碍（PD-MCI）包括不同认知领域的缺陷，在 DSM-V 之后探索神经认知障碍的一个领域是社会认知。然而，这一领域不包括目前 PD-MCI 诊断标准。此外，不同研究的测试也不同。因此，在 PD-MCI 中优化认知评估至关重要。我们旨在通过使用机器学习来实现这一目标。
纳入 275 名 PD 患者。创建了四个认知电池：两个标准电池（级别 I 和 II），应用当前标准和“传统”测试；两个替代电池（级别 I 和 II），纳入了一项社会认知测试。这些电池被纳入随机森林（RF）分类器。为了评估 RF 的性能，考虑了 AUC，估计了变量重要性指数，以了解每个测试在 PD-MCI 分类中的贡献。
标准级别 I 和 II 的 AUC 分别为 0.852 和 0.892，而替代级别 I 和 II 的 AUC 分别为 0.898 和 0.906。变量重要性指数显示 TMT B-A、埃克曼测试、RAVLT-IR、MoCA 和动作命名是对 PD-MCI 分类贡献最大的测试。
替代级别 I 的评估表现出与标准级别 II 评估相似的分类能力。这一发现表明，在 PD 患者的认知评估中，考虑到这一临床人群中受影响最大的认知领域，包括社会认知，至关重要。综上所述，这些结果表明需要修订 PD-MCI 的诊断标准。
