预测转化为精神病的临床亚型:来自上海精神病风险项目的典型相关分析研究

T. Zhang, Xiaochen Tang, Huijun Li, K. Woodberry, E. Kline, Lihua Xu, Huiru Cui, Yingying Tang, Yanyan Wei, Chunbo Li, L. Hui, M. Niznikiewicz, M. Shenton, M. Keshavan, W. Stone, Jijun Wang
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引用次数: 22

摘要

目的:由于只有30%或更少的临床高风险个体在2年内转化为精神病,因此正在努力完善风险识别策略以提高其预测能力。临床高风险是一种异质性综合征,表现为高度可变的临床症状和认知功能障碍。本研究调查了由基线临床和认知特征定义的亚型是否能改善精神病的预测。方法:从正在进行的上海精神病风险项目中招募400名临床高危受试者进行前瞻性队列研究。289例临床高危受试者在基线时完成前驱综合征结构化访谈和认知电池测试,随访至少1年,应用典型相关分析。通过典型相关分析生成典型变量,然后进行层次聚类分析生成子类型。从三个亚型构建Kaplan-Meier生存曲线,进一步检验其在预测精神病方面的效用。结果:典型相关分析确定了两种线性组合:(1)阴性症状和功能恶化相关的认知特征,(2)阳性症状和情绪紊乱相关的认知特征。聚类分析显示,三种亚型在两个维度上具有明显且相对均匀的模式,包括14.2%(亚型1,n = 41)、37.4%(亚型2,n = 108)和48.4%(亚型3,n = 140)的样本,每种亚型都具有不同的临床和认知表现特征。那些亚型1的患者,其特征是广泛的阴性症状和认知缺陷,似乎有最高的精神病风险。亚型1-3的转化风险分别为39.0%、11.1%和18.6%。结论:我们的研究结果定义了临床高危综合征中的重要亚型,这些亚型突出了超越当前诊断界限的临床症状和认知特征。这三种不同的亚型反映了临床和认知特征以及转化为精神病的风险的显著差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical subtypes that predict conversion to psychosis: A canonical correlation analysis study from the ShangHai At Risk for Psychosis program
Objective: Since only 30% or fewer of individuals at clinical high risk convert to psychosis within 2 years, efforts are underway to refine risk identification strategies to increase their predictive power. The clinical high risk is a heterogeneous syndrome presenting with highly variable clinical symptoms and cognitive dysfunctions. This study investigated whether subtypes defined by baseline clinical and cognitive features improve the prediction of psychosis. Method: Four hundred clinical high-risk subjects from the ongoing ShangHai At Risk for Psychosis program were enrolled in a prospective cohort study. Canonical correlation analysis was applied to 289 clinical high-risk subjects with completed Structured Interview for Prodromal Syndromes and cognitive battery tests at baseline, and at least 1-year follow-up. Canonical variates were generated by canonical correlation analysis and then used for hierarchical cluster analysis to produce subtypes. Kaplan–Meier survival curves were constructed from the three subtypes to test their utility further in predicting psychosis. Results: Canonical correlation analysis determined two linear combinations: (1) negative symptom and functional deterioration-related cognitive features, and (2) Positive symptoms and emotional disorganization-related cognitive features. Cluster analysis revealed three subtypes defined by distinct and relatively homogeneous patterns along two dimensions, comprising 14.2% (subtype 1, n = 41), 37.4% (subtype 2, n = 108) and 48.4% (subtype 3, n = 140) of the sample, and each with distinctive features of clinical and cognitive performance. Those with subtype 1, which is characterized by extensive negative symptoms and cognitive deficits, appear to have the highest risk for psychosis. The conversion risk for subtypes 1–3 are 39.0%, 11.1% and 18.6%, respectively. Conclusion: Our results define important subtypes within clinical high-risk syndromes that highlight clinical symptoms and cognitive features that transcend current diagnostic boundaries. The three different subtypes reflect significant differences in clinical and cognitive characteristics as well as in the risk of conversion to psychosis.
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