Romero Kristoffer, Ladyka-Wojcik Natalia, Heir Arjan, Bellana Buddhika, Leach Larry, Proulx Guy B
Department of Psychology, University of Windsor.
Department of Psychology, University of Toronto.
Arch Clin Neuropsychol. 2022 Oct 19;37(7):1480-1492. doi: 10.1093/arclin/acac043.
The diagnostic entity of mild cognitive impairment (MCI) is heterogeneous, highlighting the need for data-driven classification approaches to identify patient subgroups. However, these approaches can be strongly determined by sample characteristics and selected measures. Here, we applied a cluster analysis to an MCI patient database from a neuropsychology clinic to determine whether the inclusion of patients with MCI with vascular pathology would result in a different classification of subgroups.
Participants diagnosed with MCI (n = 166), vascular cognitive impairment-no dementia (n = 26), and a group of older adults with subjective cognitive concerns but no objective impairment (n = 144) were assessed using a full neuropsychological battery and other clinical measures. Cognitive measures were analyzed using a hierarchical cluster analysis and then a k-means approach, with resulting clusters compared on a range of demographic and clinical variables.
We found a 4-factor solution: a cognitively intact cluster, a globally impaired cluster, an amnestic/visuospatial impairment cluster, and a mild, mixed-domain cluster. Interestingly, group differences in self-reported multilingualism emerged in the derived clusters that were not observed when comparing diagnostic groups.
Our results were generally consistent with previous studies using cluster analysis in MCI. Including patients with primarily cerebrovascular disease resulted in subtle differences in the derived clusters and revealed new insights into shared cognitive profiles of patients beyond diagnostic categories. These profiles should be further explored to develop individualized assessment and treatment approaches.
轻度认知障碍（MCI）的诊断实体具有异质性，这凸显了采用数据驱动的分类方法来识别患者亚组的必要性。然而，这些方法可能会受到样本特征和所选测量方法的强烈影响。在此，我们对一家神经心理学诊所的MCI患者数据库进行了聚类分析，以确定纳入患有血管性病变的MCI患者是否会导致亚组分类有所不同。
使用全面的神经心理学测试组和其他临床测量方法，对诊断为MCI的参与者（n = 166）、血管性认知障碍非痴呆患者（n = 26）以及一组有主观认知问题但无客观损害的老年人（n = 144）进行评估。认知测量结果采用层次聚类分析，然后采用k均值法进行分析，并在一系列人口统计学和临床变量上对所得聚类进行比较。
我们发现了一个四因素解决方案：认知完整聚类、整体受损聚类、遗忘/视觉空间损害聚类和轻度混合领域聚类。有趣的是，在比较诊断组时未观察到的自我报告的多语言能力的组间差异在所得聚类中出现。
我们的结果总体上与先前在MCI中使用聚类分析的研究一致。纳入主要患有脑血管疾病的患者导致所得聚类存在细微差异，并揭示了超出诊断类别的患者共享认知特征的新见解。应进一步探索这些特征，以制定个性化的评估和治疗方法。
