A prospective study of the decision tree prediction model for attention deficit hyperactivity disorder in preschool children.
10.7499/j.issn.1008-8830.2110024
- Author:
Xin-Xin HUANG
1
;
Ping OU
1
;
Qin-Fang QIAN
1
;
Yan HUANG
1
;
Yan-Xia WANG
1
Author Information
1. Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, China).
- Publication Type:Journal Article
- Keywords:
Attention deficit hyperactivity disorder;
Decision tree;
Predictive value;
Preschool child;
Rapid screening
- MeSH:
Asians;
Attention Deficit Disorder with Hyperactivity/diagnosis*;
Child, Preschool;
Decision Trees;
Humans;
Mass Screening;
Prospective Studies
- From:
Chinese Journal of Contemporary Pediatrics
2022;24(3):255-260
- CountryChina
- Language:English
-
Abstract:
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.
