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聚类分析的阴性症状确定不同的阴性症状亚组。

Cluster analysis of negative symptoms identifies distinct negative symptom subgroups.

机构信息

Department of Psychology, University of Nevada, Las Vegas, United States of America.

Department of Psychology, University of Georgia, United States of America.

出版信息

Schizophr Res. 2022 Aug;246:207-215. doi: 10.1016/j.schres.2022.06.021. Epub 2022 Jul 6.

DOI:10.1016/j.schres.2022.06.021
PMID:35809353
Abstract

The heterogeneity of schizophrenia has been acknowledged for decades because of the diverse presentation of symptoms, illness course, and treatment response noted between individuals diagnosed with the disorder. Cluster analysis has been used as a statistical method to determine whether schizophrenia subgroups might be identified based on symptom heterogeneity. However, there is very limited research examining whether heterogeneity in negative symptoms might be useful in establishing schizophrenia subtypes, particularly research examining newer models of negative symptoms based on five latent constructs including anhedonia, asociality, avolition, blunted affect, and alogia. The Brief Negative Symptom Scale was used to assess the five negative symptoms domains in a sample of 220 outpatients diagnosed with schizophrenia or schizoaffective disorder. Cluster analysis supported a four-cluster solution, comprising clusters of subjects with low negative symptoms (LNS), severe negative symptoms (SNS), and two clusters with moderate negative symptoms, one with predominantly elevated blunted affect (BA) and one with elevated avolition (AV). The LNS, SNS, BA, and AV clusters significantly differed on external validators including clinical characteristics, neurocognition, and functional outcome. Findings suggest that schizophrenia heterogeneity can be parsed according to negative symptom subtypes that have distinct clinical and neuropsychological profiles. Implications for diagnosis and treatment are discussed.

摘要

精神分裂症的异质性已经得到了几十年的承认,因为在被诊断患有该疾病的个体之间,症状、疾病过程和治疗反应的表现各不相同。聚类分析已被用作一种统计方法,以确定是否可以根据症状的异质性来确定精神分裂症亚组。然而,很少有研究探讨阴性症状的异质性是否有助于建立精神分裂症亚型,特别是基于包括快感缺失、社交回避、意志缺失、情感迟钝和言语贫乏在内的五个潜在结构的新的阴性症状模型的研究。在一项由 220 名被诊断为精神分裂症或分裂情感障碍的门诊患者组成的样本中,使用简短阴性症状量表评估了五个阴性症状领域。聚类分析支持了一个四聚类解决方案,包括阴性症状低的患者聚类(LNS)、严重阴性症状聚类(SNS)以及两个中等阴性症状聚类,一个聚类以明显升高的情感迟钝为主(BA),另一个聚类以升高的意志缺失为主(AV)。LNS、SNS、BA 和 AV 聚类在包括临床特征、神经认知和功能结果在内的外部验证器上有显著差异。研究结果表明,根据具有不同临床和神经心理学特征的阴性症状亚型,可以对精神分裂症的异质性进行细分。讨论了对诊断和治疗的影响。

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Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis.
采用聚类分析探讨诊断为精神分裂症和分裂情感性障碍患者的阴性症状异质性。
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Deconstructing heterogeneity in schizophrenia through language: a semi-automated linguistic analysis and data-driven clustering approach.通过语言剖析精神分裂症的异质性:一种半自动语言分析和数据驱动聚类方法。
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