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首页> 外文期刊>Journal of child psychology and psychiatry >Investigating phenotypic heterogeneity in children with autism spectrum disorder: A factor mixture modeling approach
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Investigating phenotypic heterogeneity in children with autism spectrum disorder: A factor mixture modeling approach

机译:调查自闭症谱系障碍儿童的表型异质性:因素混合建模方法

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Background: Autism spectrum disorder (ASD) is characterized by notable phenotypic heterogeneity, which is often viewed as an obstacle to the study of its etiology, diagnosis, treatment, and prognosis. On the basis of empirical evidence, instead of three binary categories, the upcoming edition of the DSM 5 will use two dimensions - social communication deficits (SCD) and fixated interests and repetitive behaviors (FIRB) - for the ASD diagnostic criteria. Building on this proposed DSM 5 model, it would be useful to consider whether empirical data on the SCD and FIRB dimensions can be used within the novel methodological framework of Factor Mixture Modeling (FMM) to stratify children with ASD into more homogeneous subgroups. Methods: The study sample consisted of 391 newly diagnosed children (mean age 38.3 months; 330 males) with ASD. To derive subgroups, data from the Autism Diagnostic Interview-Revised indexing SCD and FIRB were used in FMM; FMM allows the examination of continuous dimensions and latent classes (i.e., categories) using both factor analysis (FA) and latent class analysis (LCA) as part of a single analytic framework. Results: Competing LCA, FA, and FMM models were fit to the data. On the basis of a set of goodness-of-fit criteria, a 'two-factor/three-class' factor mixture model provided the overall best fit to the data. This model describes ASD using three subgroups/classes (Class 1: 34%, Class 2: 10%, Class 3: 56% of the sample) based on differential severity gradients on the SCD and FIRB symptom dimensions. In addition to having different symptom severity levels, children from these subgroups were diagnosed at different ages and were functioning at different adaptive, language, and cognitive levels. Conclusions: Study findings suggest that the two symptom dimensions of SCD and FIRB proposed for the DSM 5 can be used in FMM to stratify children with ASD empirically into three relatively homogeneous subgroups.
机译:背景:自闭症谱系障碍(ASD)具有明显的表型异质性,通常被视为对其病因,诊断,治疗和预后研究的障碍。根据经验证据,DSM 5的下一版将取代三个二元类别,将使用两个维度-社会沟通缺陷(SCD)和固定利益与重复行为(FIRB)-作为ASD诊断标准。在此建议的DSM 5模型的基础上,考虑是否可以在因子混合模型(FMM)的新方法框架内使用SCD和FIRB维度上的经验数据,以将ASD患儿分为更均一的亚组。方法:研究样本包括391名新诊断的ASD儿童(平均年龄38.3个月; 330例男性)。为了得出亚组,在FMM中使用了自闭症诊断访谈修订索引SCD和FIRB的数据。 FMM允许使用因子分析(FA)和潜在类分析(LCA)作为单个分析框架的一部分来检查连续维度和潜在类(即类别)。结果:竞争的LCA,FA和FMM模型适合数据。根据一套拟合优度标准,“两因素/三类”因素混合模型为数据提供了总体最佳拟合。该模型基于SCD和FIRB症状维度上的严重程度梯度,使用三个子组/类别(样本的1类:34%,2类:10%,3类:56%)描述了ASD。除了具有不同的症状严重程度水平外,这些亚组的儿童还被诊断为不同年龄,并且以不同的适应性,语言和认知水平起作用。结论:研究结果表明,针对DSM 5提出的SCD和FIRB的两个症状维度可用于FMM,从经验上将ASD儿童分为三个相对同质的亚组。

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