描述从轻度认知障碍到痴呆症的进展:纵向临床访问的网络分析
Muskan Garg1, Sara Hejazi1,2,3, Sunyang Fu1,4
1Department of Artificial Intelligence & Informatics, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
BMC medical informatics and decision making
|October 18, 2024
概括
网络分析揭示了慢性疾病和神经精神症状在从轻度认知障碍到痴呆症的进展过程中是如何相关的. 这种方法突出了影响痴呆症发病的关键因素,并指出了性别特异性的显著差异.
科学领域:
- 神经科学是一个神经科学.
- 老年学是一门学科.
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录越来越多地用于认知衰退研究.
- 以前的研究重点是预测痴呆症,而不是随着时间的推移其进展或风险因素的关联.
- 机器学习模型引发了人们对医疗保健决策透明度和安全性的担忧.
研究的目的:
- 描述健康状况从轻度认知障碍 (MCI) 到痴呆症发作的进展.
- 通过网络分析分析慢性疾病和神经精神症状的变化.
- 确定影响痴呆症发病的关键变量,并探索性别特异性影响.
主要方法:
- 利用了梅奥诊所衰老研究 (MCSA) 的数据,这是一个潜在的基于人口的队列.
- 应用网络分析来检查从MCI诊断到痴呆症发作的健康变量之间不断变化的关联.
- 确定了导致痴呆症发病的网络社区和重要变量,包括性别特定的分析.
主要成果:
- 在整个MCI中发现神经精神症状和慢性疾病之间的相关性,导致痴呆症的进展.
- 观察到痴呆症进展的个体的认知/功能评分发生显著变化.
- 确定了临床痴呆症评级,功能活动,慢性病和NPI-Q得分对痴呆症发病的影响的性别特异性差异.
结论:
- 网络分析有效地捕捉了从MCI到痴呆症进展的纵向健康变化.
- 这种方法为监测认知障碍提供了有价值的工具.
- 在特定的健康状况如何影响痴呆症进展方面存在着显著的性别差异.
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