Early response to SPN-812 (viloxazine extended-release) can predict efficacy outcome in pediatric subjects with ADHD: a machine learning post-hoc analysis of four randomized clinical trials.
Journal:
Psychiatry research
PMID:
33418457
Abstract
Machine learning (ML) was used to determine whether early response can predict efficacy outcome in pediatric subjects with ADHD treated with SPN-812. We used data from four Phase 3 placebo-controlled trials of 100- to 600-mg/day SPN-812 (N=1397; 6-17 years of age). The treatment response was defined as having a ≥50% reduction in change from baseline (CFB) in ADHD Rating Scale-5 (ADHD-RS-5) Total score at Week 6. The variables used were: ADHD-RS-5 Total score, age, body weight, and body mass index at baseline; CFB ADHD-RS-5 Total score at Week 1, cumulative change in ADHD-RS-5 Total score at Week 2, and cumulative change in ADHD-RS-5 Total score at Week 3; Clinical Global Impressions-Improvement (CGI-I) score at Week 1, 2, and 3; and target dose. Using the best selected model, lasso regression, to generate importance scores, we found that change in ADHD-RS-5 Total score and CGI-I score were the best predictors of efficacy outcome. Change in ADHD-RS-5 Total score at Week 2 could predict treatment response at Week 6 (75% positive predictive power, 75% sensitivity, 74% specificity). Therefore, early response after two weeks of treatment with once-daily SPN-812 in pediatric patients with ADHD can predict efficacy outcome at Week 6.
Authors
Keywords
Adolescent
Attention Deficit Disorder with Hyperactivity
Body Mass Index
Body Weight
Central Nervous System Stimulants
Child
Clinical Trials as Topic
Delayed-Action Preparations
Dose-Response Relationship, Drug
Double-Blind Method
Drug Administration Schedule
Female
Humans
Machine Learning
Male
Randomized Controlled Trials as Topic
Treatment Outcome
Viloxazine