Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and
Ting Gao1, Lan Yang2, Jiayu Zhou3
1Department of Rehabilitation, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou 510623, China.
Insights
A new nomogram tool helps identify children at high risk for Attention-Deficit/Hyperactivity Disorder (ADHD). This reliable model aids early ADHD recognition and supports family management strategies.
Area of Science:
- Child Psychology
- Medical Informatics
- Public Health
Background:
- Attention-Deficit/Hyperactivity Disorder (ADHD) poses challenges for early identification and management.
- A user-friendly, multidimensional prediction tool is needed to assess ADHD risk in children.
- Early recognition facilitates timely intervention and family support.
Purpose of the Study:
- To develop and validate a risk nomogram for ADHD in children aged 3-17 years in the United States.
- To base the prediction tool on clinical manifestations and complex environmental factors.
- To provide a reliable instrument for individualized ADHD risk assessment.
Main Methods:
- Utilized a large dataset of 141,356 cases for model development.
- Performed independent external validation on a separate dataset of 54,444 cases.
- Employed LASSO regression and logistic regression for variable selection and model establishment, validated using bootstrapping.
Main Results:
- Developed a risk nomogram for ADHD incorporating 13 independent predictors.
- Key predictors include behavioral problems, learning disabilities, age, intellectual disabilities, anxiety, gender, premature birth, maternal age, and parent-child interaction.
- The model demonstrated strong predictive accuracy with a C-index of 0.887 (training) and 0.862 (validation).
Conclusions:
- The validated nomogram serves as a reliable statistical tool for assessing individualized ADHD risk in children.
- Facilitates early identification of high-risk children.
- Supports the development of collaborative society-family-school models for ADHD diagnosis, treatment, and management.
Objectives:
A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3-17 years in the United States based on clinical manifestations and complex environments.
Methods:
A total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples.
Results:
A final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable.
Conclusions:
A nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD.
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