Generalizable prediction of childhood ADHD symptoms from neurocognitive testing and youth characteristics

crossref(2022)

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摘要
Importance: Childhood Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms are linked to many negative outcomes and widely believed to result from disrupted neurocognitive development. However, evidence for the clinical value of neurocognitive assessments in this context has been mixed and there have been no large-scale efforts to quantify the potential of neurocognitive abilities, along with data from other domains (e.g., child and family characteristics, demographics), for use in generalizable machine learning models that predict individual youths’ ADHD symptoms in independent data.Objective: To develop and test, in a large and diverse sample of youth, cross-validated machine learning models for predicting youths’ ADHD symptoms using demographics, child and family characteristics, and neurocognitive assessments. Design, Setting, and Participants: Data were from participants of the Adolescent Brain Cognitive Development Study (ABCD), a collaborative consortium that recruited a diverse sample of youth (ages 9-10 at baseline) across 21 sites in the United States. Participants with complete parent and teacher reports of ADHD symptoms at the 1-year follow-up (n = 5,900) were included.Exposures: Predictor variables were from the ABCD baseline timepoint and included basic demographic and biometric measures, geocoded community and neighborhood data, youths’ self-report about child and family characteristics, and neurocognitive tests.Main Outcome: Models predicted a latent cross-rater ADHD symptoms factor derived from a bifactor model of parent and teacher ratings at the 1-year follow-up.Results: Predictive models explained about 20% of the variance in ADHD symptoms for ABCD Study sites that were left out of the model-fitting process, with high generalizability across sites and only trivial loss of predictive power when applied to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, screen time, parental monitoring, school involvement, and socioeconomic disadvantage. A sparse model including only seven neurocognitive measures, male sex, screen time, and two impulsivity scales displayed comparable performance to larger models.Conclusions and Relevance: This work quantifies the informational value of neurocognitive abilities and other child characteristics for predicting individual children’s ADHD symptoms in unseen data and provides a foundational method for the prediction of ADHD symptoms in ABCD and across other research and clinical contexts.
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