Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study.

Objective: To reduce the incidence of Opioid Use Disorder (OUD), multiple guidelines recommend assessing the risk of OUD prior to prescribing oral opioids. Although subjective risk assessments are available to help classify subjects at risk for OUD, we are aware of no clinically validated objective risk assessment tools. An objective risk assessment based on genetics may help inform shared decision-making prior to prescribing short-duration oral opioids.

Methods: A multicenter, observational cohort of adults exposed to prescription oral opioids for 4-30 days was conducted to determine the performance of an OUD classifier derived from machine learning (ML). From this cohort, the demographics of the U.S. adult opioid-prescribed population were used to create a blinded, random, representative group of subjects (n=385) for analysis to accurately estimate the performance characteristics in the intended use population. Genotyping was performed via a qualitative SNP microarray on DNA extracted from buccal samples.

Results: In the study subjects, the classifier demonstrated 82.5% sensitivity (95% confidence intervals: 76.1%-87.8%) and 79.9% specificity (73.7-85.2%), with no statistically significant differences in clinical performance observed based on gender, age, length of follow-up from opioid exposure, race, or ethnicity.

Conclusion: This study demonstrates an ML classifier may provide additional objective information regarding a patient's risk of developing OUD. This information may enable subjects and healthcare providers to make more informed decisions when considering the use of oral opioids.