Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors

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.