Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; Haidian District Mental Health Prevent-Treatment Hospital, Beijing, China.
Haidian District Mental Health Prevent-Treatment Hospital, Beijing, China.
Schizophr Res. 2018 Dec;202:248-253. doi: 10.1016/j.schres.2018.07.005. Epub 2018 Jul 8.
The present study aimed to validate a severity cut-off of negative symptoms for persistent negative symptoms (PNS) identification using the Clinical Assessment Interview for Negative Symptoms (CAINS).
A total of 206 patients with schizophrenia were recruited and divided into the PNS group (n = 57) and the Non-PNS group (n = 149) using PNS criteria based on the SANS and the SAPS. To determine the appropriate cut-offs on the CAINS in identifying PNS, Receiver Operating Characteristic (ROC) curve analysis was conducted in the PNS and Non-PNS groups.
Our results showed that the cutoffs for identifying PNS on the CAINS total score, the Motivation and Pleasure (MAP) subscale score and the Expression (EXP) subscale score were 25, 17, and 5 respectively. Area Under the Curve (AUC) analysis indicated excellent discrimination of the PNS group from the Non-PNS group using the cut-off for the CAINS total score. However, discrimination was somewhat better for the MAP subscale score than the EXP subscale score. The Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of the MAP subscale were 81.54% and 97.16%.
We found that the cut-off scores derived from the CAINS to identify PNS are comparable to existing scales. The CAINS offers an alternative means in identifying PNS patients in clinical trials that overcomes methodological and conceptual limitations of older scales.
本研究旨在使用临床负性症状评估访谈（CAINS）验证用于持续性负性症状（PNS）识别的负性症状严重程度截断值。
共招募了 206 名精神分裂症患者，根据 SANS 和 SAPS 的 PNS 标准，将其分为 PNS 组（n=57）和非 PNS 组（n=149）。为了确定 CAINS 中用于识别 PNS 的适当截断值，在 PNS 和非 PNS 组中进行了受试者工作特征（ROC）曲线分析。
我们的结果表明，CAINS 总分、动机和愉悦（MAP）分量表得分和表达（EXP）分量表得分的截断值分别为 25、17 和 5。曲线下面积（AUC）分析表明，使用 CAINS 总分的截断值可以很好地区分 PNS 组和非 PNS 组。然而，MAP 分量表得分的区分度略优于 EXP 分量表得分。MAP 分量表的阳性预测值（PPV）和阴性预测值（NPV）分别为 81.54%和 97.16%。
我们发现，CAINS 用于识别 PNS 的截断值与现有量表相当。CAINS 为在临床试验中识别 PNS 患者提供了一种替代方法，克服了旧量表在方法学和概念上的局限性。
