在早期精神病的风险评估中轻度认知缺陷的模型
TianHong Zhang1, HuiRu Cui1, XiaoChen Tang1
1Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China.
Psychological medicine
|March 4, 2024
概括
量身定制的轻度认知缺陷 (MCD) 模型可以改善精神病风险预测. 针对不同早期精神病阶段和不同人群的优化模型提供了比一般方法更精确的评估.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 认知心理学 认知心理学
背景情况:
- 轻度认知缺陷 (MCD) 存在于精神病 (FEP) 的第一个发作之前和临床高风险 (CHR) 阶段.
- 准确预测精神病发作和进展对于早期干预至关重要.
研究的目的:
- 开发和验证针对特定早期精神病阶段 (CHR,FEP) 和人群量身定制的轻度认知缺陷 (MCD) 模型.
- 为了提高预测危险人群中精神病转换的准确性.
- 使用认知概况,区分健康对照 (HC),CHR和FEP组.
主要方法:
- 通过使用 MATRICS 共识认知电池,评估了一大群1059名FEP,794名CHR和774名匹配HC的个人.
- 对CHR个体进行了2年的跟踪,以确定转换者 (CHR-C) 和非转换者 (CHR-NC).
- 用接收器操作特征 (ROC) 分析来评估区分组中的模型性能.
主要成果:
- 与HC相比,CHR和FEP组在所有测试领域都显示出显著的认知缺陷 (p < 0.001).
- 在视觉空间记忆,迷宫和符号编码测试中,CHR转换器 (CHR-C) 的表现比非转换器 (CHR-NC) 更差 (p < 0.023).
- 一个一般的MCD模型显示了FEP和HC之间的优异区别 (AUC=0.895),但对预测CHR转换 (AUC=0.581) 不令人满意.
- 三种量身定制的MCD模型实现了高准确度:89.3%的FEP与HC,65.6%的CHR-C与CHR-NC预测,和80.2%的CHR与HC识别.
结论:
- 轻度认知缺陷在不同早期精神病阶段和不同人群的模式和意义上有所不同.
- 定制的MCD模型对于早期精神病的精确风险评估和预测至关重要.
- 优化的认知模型可以显著改善对患精神病高风险个体的识别.
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