Danish National Research Foundation: Center for Subjectivity Research, University of Copenhagen, Copenhagen, Denmark.
Schizophr Res. 2011 Apr;127(1-3):107-14. doi: 10.1016/j.schres.2010.12.021. Epub 2011 Feb 4.
Improving the identification of clinical vulnerability to psychosis in help-seeking subjects is crucial for refining risk stratifications and implementing intervention strategies.
To define underlying dimensions of subclinical psychopathology in Ultra-High-Risk (UHR) subjects; to test their temporal stability and association with baseline clinical and functional features; and to evaluate their predictive value for subsequent transition to psychosis.
223 subjects meeting the Personal Assessment and Crisis Evaluation (PACE) criteria for UHR were assessed with the Comprehensive Assessment of At-Risk Mental States (CAARMS) and monitored for a period of up to three years. Data were analysed via principal component analysis (PCA), Spearman correlation analysis and Cox regression.
PCA of the CAARMS yielded three orthogonal symptom clusters (negative, disorganized and perceptual-affective instability) with substantial temporal stability over a one-month time span. These clusters were strongly related to global functioning, quality of life, baseline major psychopathology and duration of symptoms before referral. The severity of the CAARMS disorganized component was the strongest predictor of transition to frank psychosis at follow-up.
A dimensional approach to CAARMS-measured symptoms may refine current early identification heuristics and provide an alternative way to characterize UHR profiles complementary to the current categorical one.
提高对寻求帮助的个体出现精神病临床脆弱性的识别能力，对于完善风险分层和实施干预策略至关重要。
定义超高危（UHR）人群中亚临床精神病理学的潜在维度；检验其时间稳定性及其与基线临床和功能特征的关系；并评估其对随后发展为精神病的预测价值。
对符合个人评估和危机评估（PACE）UHR 标准的 223 名受试者进行全面的风险精神状态评估（CAARMS）评估，并进行长达三年的监测。通过主成分分析（PCA）、Spearman 相关分析和 Cox 回归分析来处理数据。
CAARMS 的 PCA 产生了三个正交的症状群（阴性、紊乱和知觉-情感不稳定），在一个月的时间跨度内具有很大的时间稳定性。这些聚类与整体功能、生活质量、基线主要精神病理学和转诊前症状持续时间密切相关。CAARMS 紊乱成分的严重程度是后续出现明显精神病的最强预测因子。
用 CAARMS 测量的症状进行维度分析可能会完善当前的早期识别启发式方法，并提供一种替代方法来描述 UHR 特征，这与当前的分类方法互为补充。
