OBJECTIVES 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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脑血管病理学对轻度认知障碍患者神经心理评分聚类分析的影响。

目标 轻度认知障碍 (MCI) 的诊断实体具有异质性，强调需要数据驱动的分类方法来识别患者亚组。然而，这些方法可以强烈地由样本特征和选定的措施决定。在这里，我们对来自神经心理学诊所的 MCI 患者数据库应用聚类分析，以确定是否包含具有血管病理学的 MCI 患者会导致不同的亚组分类。方法 被诊断为 MCI (n = 166)、血管性认知障碍 - 无痴呆 (n = 26) 和一组有主观认知问题但没有客观障碍的老年人 (n = 144) 使用完整的神经心理学电池和其他临床措施。使用层次聚类分析和 k-means 方法分析认知测量，并在一系列人口统计学和临床​​变量上比较所得聚类。结果 我们发现了一个 4 因素解决方案：认知完整集群、整体受损集群、遗忘/视觉空间障碍集群和轻度混合域集群。有趣的是，在比较诊断组时未观察到的衍生集群中出现了自我报告的多语种的组差异。结论 我们的结果与之前在 MCI 中使用聚类分析的研究基本一致。包括主要患有脑血管疾病的患者导致衍生集群的细微差异，并揭示了对超出诊断类别的患者共享认知概况的新见解。