Early response to viloxazine extended-release (viloxazine ER, Qelbree®) treatment predicted efficacy outcome in pediatric subjects with attention-deficit/hyperactivity disorder (ADHD). This study sought to determine whether the machine learning lasso model used in the pediatric study would predict response to viloxazine ER in an adult population based on early improvements in ADHD symptoms. We used data from a double-blind, placebo-controlled, flexible-dose (200-600 mg) study of viloxazine ER (N = 354; 18 to 60 years old). Area under the Receiver Operating Characteristic Curve (ROC AUC) statistics were computed using the lasso model from pediatric data to predict responder status in adults. Response was defined as ≥50% reduction from baseline in the Adult ADHD Investigator Symptoms Rating Scale (AISRS) Total score at Week 6. The adult study sample included 127 viloxazine ER-treated subjects with Week 6 data. Fifty-one subjects (40.2%) were categorized as responders. The ROC curves indicated that data collected up to Week 2 were sufficient to accurately predict treatment response at Week 6 with 68% positive predictive power, 80% sensitivity, and 74% specificity. This analysis demonstrated that the predictive model estimated from the child data generalizes to adults with ADHD, further supporting the consistency of viloxazine ER treatment across age groups.
Keywords: ADHD; AISRS; Machine learning; Predictor; Qelbree®; Treatment response.
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