将PDD行为量表用作二级筛查工具：分类与回归树分析

Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis.

作者信息

Cohen Ira L, Liu Xudong, Hudson Melissa, Gillis Jennifer, Cavalari Rachel N S, Romanczyk Raymond G, Karmel Bernard Z, Gardner Judith M

机构信息

Department of Psychology, New York State Institute for Basic Research in Developmental Disabilities, 1050 Forest Hill Road, Staten Island, NY, 10314, USA.

Queen's Genomics Lab at Ongwanada, Ongwanada Resource Center, Department of Psychiatry, Queen's University, 191 Portsmouth Ave, Kingston, ON, K7M 8A6, Canada.

出版信息

J Autism Dev Disord. 2016 Sep;46(9):3006-22. doi: 10.1007/s10803-016-2843-0.

DOI:10.1007/s10803-016-2843-0

PMID:27318809

Abstract

In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80 %, generalized to an independent validation set, and generalized across age groups and sites, and agreed well with ADOS classifications. Parent PDDBIs yielded better results than teacher PDDBIs but, when CART predictions agreed across informants, sensitivity increased. Results also revealed three subtypes of ASD: minimally verbal, verbal, and atypical; and two, relatively common subtypes of non-ASD children: social pragmatic problems and good social skills. These subgroups corresponded to differences in behavior profiles and associated bio-medical findings.

摘要

为了提高自闭症谱系障碍（ASD）与类似神经发育障碍之间的辨别准确性，对大量有或无ASD儿童的广泛性发育障碍行为量表（PDDBI）表格的多中心样本使用了一种数据挖掘程序——分类与回归树（CART）。辨别准确率超过80%，并推广到一个独立的验证集，且在不同年龄组和不同地点都能得到推广，与自闭症诊断观察量表（ADOS）分类结果高度一致。家长填写的PDDBI比教师填写的产生了更好的结果，但是，当CART预测在不同信息提供者之间达成一致时，敏感性会提高。结果还揭示了ASD的三种亚型：极少言语型、言语型和非典型型；以及非ASD儿童的两种相对常见的亚型：社交语用问题型和好社交技能型。这些亚组对应于行为特征和相关生物医学发现的差异。