A prospective study of the decision tree prediction model for attention deficit hyperactivity disorder in preschool
Xin-Xin Huang1, Ping Ou1, Qin-Fang Qian1
1Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, China).
Insights
This study shows that combining attention time with behavior scale assessments can accurately screen for attention deficit hyperactivity disorder (ADHD) in preschool children. The developed decision tree model offers a reliable tool for rapid ADHD identification in clinical settings.
Area of Science:
- Pediatric Psychiatry
- Neurodevelopmental Disorders
- Clinical Psychology
Background:
- Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children.
- Early and accurate screening of ADHD in preschool children is crucial for timely intervention.
- Existing screening methods may require further refinement for improved efficiency and accuracy.
Purpose of the Study:
- To evaluate the clinical utility of integrating attention time measurements with behavior scale assessments for ADHD screening in preschool-aged children.
- To develop and validate a decision tree model for enhanced ADHD detection.
Main Methods:
- A case-control study involving 200 preschool children diagnosed with ADHD and 200 controls.
- Data collection included recording attention time and administering the Chinese Version of Swanson Nolan and Pelham, Version IV Scale-Parent Form (SNAP-IV).
- Decision tree analysis was employed to assess the combined predictive value of attention time and SNAP-IV scores, using clinical diagnosis as the gold standard.
Main Results:
- Children with ADHD exhibited significantly shorter attention times and higher scores on specific SNAP-IV items compared to controls (P<0.05).
- The developed decision tree model achieved an overall accuracy of 75% in predicting ADHD (81% for ADHD, 69% for non-ADHD).
- The model demonstrated a significant area under the ROC curve of 0.816 (95% CI: 0.774-0.857, P<0.001), indicating strong discriminative ability.
Conclusions:
- A decision tree model incorporating attention time and behavior scale (SNAP-IV) results provides a highly accurate method for screening ADHD in preschool children.
- This combined approach facilitates rapid and effective ADHD screening in clinical practice.
- The findings support the use of this model for early identification and intervention planning for preschool children with ADHD.
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.
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