Objectives: To study the clinical value of attention time combined with behavior scale in the screening of attention deficit hyperactivity disorder (ADHD) in preschool children.
Methods: A total of 200 preschool children with ADHD diagnosed in Fujian Maternal and Child Health Hospital from February 2019 to March 2020 were enrolled as the ADHD group. A total of 200 children who underwent physical examination in the hospital or kindergartens during the same period were enrolled as the control group. Attention time was recorded. Chinese Version of Swanson Nolan and Pelham, Version IV Scale-Parent Form (SNAP-IV) scale was used to evaluate symptoms. With clinical diagnosis as the gold standard, the decision tree analysis was used to evaluate the clinical value of attention time combined with behavior scale in the screening of ADHD.
Results: Compared with the control group, the ADHD group had significantly higher scores of SNAP-IV items 1, 4, 7, 8, 10, 11, 14, 15, 16, 18, 20, 21, and 22 (P<0.05) and a significantly shorter attention time (P<0.05). The variables with statistically significant differences between the two groups in univariate analysis were used as independent variables to establish a decision tree model. The accuracy of the model in predicting ADHD was 81%, that in predicting non-ADHD was 69%, and the overall accuracy was 75%, with an area under the ROC curve of 0.816 (95% CI: 0.774-0.857, P<0.001).
Conclusions: The decision tree model for screening ADHD in preschool children based on attention time and assessment results of behavior scale has a high accuracy and can be used for rapid screening of ADHD among children in clinical practice.
目的: 探讨注意力时间联合行为量表在学龄前儿童注意缺陷多动障碍（attention deficit hyperactivity disorder，ADHD）筛检中的应用价值。方法: ADHD组来自2019年2月至2020年3月福建省妇幼保健院确诊的学龄前ADHD儿童200例，对照组来自同期同医院或幼儿园体检的儿童200例，记录注意力时间，并使用中文版SNAP-Ⅳ评定量表父母版（Chinese Version of Swanson Nolan and Pelham，Version Ⅳ Scale-Parent Form）评估症状。以临床诊断为金标准，应用决策树分析法，评估注意力时间联合行为量表筛查ADHD的临床应用价值。结果: ADHD组SNAP-Ⅳ条目1、4、7、8、10、11、14、15、16、18、20、21、22的得分高于对照组（P<0.05），注意力时间短于对照组（P<0.05）。将单因素分析2组间差异有统计学意义的变量作为自变量制定决策树模型，该模型预测ADHD的准确率为81%，预测非ADHD的准确率为69%，总体准确率为75%，受试者工作特征曲线下面积为0.816（95%CI：0.774~0.857，P<0.001）。结论: 基于注意力时间和行为量表建立的学龄前儿童ADHD筛查的决策树模型准确性较高，可用于临床快速进行儿童ADHD初筛，促进开展全人口ADHD筛查与管理。.
Keywords: Attention deficit hyperactivity disorder; Decision tree; Predictive value; Preschool child; Rapid screening.