Toward personalized smoking-cessation treatment: Using a predictive modeling approach to guide decisions regarding stimulant medication treatment of attention-deficit/hyperactivity disorder (ADHD) in smokers.

Background And Objectives: Osmotic-release oral system methylphenidate (OROS-MPH) did not show overall benefit as an adjunct smoking cessation treatment for adult smokers with ADHD in a randomized, placebo-controlled, multicenter clinical trial. A secondary analysis revealed a significant interaction between ADHD symptom severity and treatment-response to OROS-MPH, but did not account for other baseline covariates or estimate the magnitude of improvement in outcome if treatment were optimized. This present study addressed the gaps in how this relationship should inform clinical practice.

Methods: Using data from the Adult Smokers with ADHD Trial (N = 255, six sites in five US States), we build predictive models to calculate the probability of achieving prolonged abstinence, verified by self-report, and expired carbon monoxide measurement. We evaluate the potential improvement in achieving prolonged abstinence with and without stratification on baseline ADHD severity.

Results: Predictive modeling demonstrates that the interaction between baseline ADHD severity and treatment group is not affected by adjusting for other baseline covariates. A clinical trial simulation shows that giving OROS-MPH to patients with baseline Adult ADHD Symptom Rating Scale (ADHD-RS) >35 and placebo to those with ADHD-RS ≤35 would significantly improve the prolonged abstinence rate (52 ± 8% vs. 42 ± 5%, p < .001).

Conclusions And Scientific Significance: In smokers with ADHD, utilization of a simple decision rule that stratifies patients based on baseline ADHD severity can enhance overall achievement of prolonged smoking abstinence. Similar analysis methods should be considered for future clinical trials for other substance use disorders.