Analysis of influencing factors of attention deficit hyperactivity disorder in children from 7 to 16 years old and the establishment and verification of Nomogram prediction model
10.3760/cma.j.cn101070-20210809-00945
- VernacularTitle:注意缺陷多动障碍7~16岁儿童影响因素分析及Nomogram预测模型的建立与验证
- Author:
Xiaoyan GAO
1
;
Wufu DILINUER
;
Pengxiang ZUO
;
Fangfang LIU
;
Hongyao HE
Author Information
1. 石河子大学医学院，石河子 832000
- Keywords:
Child;
Attention deficit hyperactivity disorder;
Influencing factor;
Nomogram
- From:
Chinese Journal of Applied Clinical Pediatrics
2022;37(13):1001-1005
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the influencing factors of attention deficit hyperactivity disorder (ADHD) in children and construct a Nomogram prediction model.Methods:A total of 5 409 children aged 7 to 16 from 5 schools in Xinjiang were investigated by using SNAP-Ⅳ assessment scale and influencing factors questionnaire.Least absolute shrinkage and selection operator (LASSO) regression and multivariate Logistic regression were used to analyze and investigate the influencing factors of ADHD in children, and then Nomogram prediction model was established. Results:(1)The detection rate of ADHD was 7.3%.(2) The LASSO- Logistic regression model showed that the history of febrile convulsions ( OR=5.97, 95% CI: 3.52-9.86), the history of epilepsy disease ( OR=11.86, 95% CI: 7.83-17.89), the history of head trauma disease ( OR=10.0, 95% CI: 7.27-13.71), mother′s delivery method ( OR=2.53, 95% CI: 1.99-3.23), mother′s education level ( OR=2.26, 95% CI: 1.45-3.67), mother′s smoking more than 1 year ( OR=12.65, 95% CI: 8.30-19.34), whether the family environment is quiet ( OR=1.27, 95% CI: 1.00-1.63), and the education method of beating and scolding ( OR=3.05, 95% CI: 2.13-4.31) was an indepen-dent risk factor for children with ADHD; (3)The Nomogram prediction model was built and verified by Bootstrap for 1 000 samples.The C-index was 0.81(95% CI: 0.78-0.83), suggesting that the Nomogram prediction model has good prediction ability, accuracy, and distinction.Decision curve analysis (DCA) of the clinical decision curve suggested that patients with Nomogram model with a predictive probability threshold greater than 0.2 had a higher clinical net benefit. Conclusions:The detection rate of ADHD was 7.3%, which was higher than the national average.The Nomogram prediction model drawn here can provide individualized ADHD risk predictions for children based on the history of hyperthermia, epilepsy, and head trauma, maternal mode of childbirth, maternal education level, maternal education level, maternal smoking for more than 1 year, quiet family environment, and scolding education methods.
