25. Efficiently Identifying Adolescents in Need of Substance Use Disorder Treatment: Testing an Expanded Computerized Adaptive Test in an Adolescent Sample

Few adolescents with substance use disorders (SUDs) receive SUD treatment, partly due to poor identification of youth with SUDs across healthcare and other youth-serving settings. While brief screeners can be rapidly completed by adolescents and detect problematic substance use, screeners cannot provide SUD diagnoses. Structured interviews can accurately diagnose SUDs but are time- and resource-intensive, limiting their feasibility and scalability. Computerized adaptive tests (CATs), which apply multidimensional item response theory and adaptively select a subset of items from a larger item bank based upon each person’s responses, may address the need for an assessment tool with minimal time, patient, and clinician burden and increased diagnostic accuracy. A CAT for SUDs (CAT-SUD-E) has been validated in adult populations but has yet to be tested in an adolescent sample. The purpose of this study was to perform the first evaluation of the CAT-SUD-E in an adolescent sample compared to a gold standard diagnostic interview. Adolescents aged 11-17 with a diverse set of substance use histories were recruited from the community. Participants (N = 156; age, M=14.7, SD=1.99; 48.7% male) completed the CAT-SUD-E electronically and the substance related disorders portion of a gold standard clinician-conducted diagnostic interview (K-SADS) via tele-videoconferencing platform. The CAT-SUD-E for adolescents computed a continuous severity score and assessed both present (past 30 days) and lifetime (prior to the past 30 days) overall SUD and substance-specific diagnoses for nine substance classes (e.g., alcohol, cannabis, nicotine/tobacco). Logistic regression analyses were used to test the association between the CAT-SUD-E and K-SADS, with the CAT-SUD-E continuous severity score and diagnoses as predictors and the K-SADS SUD diagnoses as outcomes. Using the CAT-SUD-E (severity score and diagnoses) to predict the presence of any K-SADS SUD diagnosis, the classification accuracy ranged from excellent for current SUD (AUC=0.89, 95% CI=0.81, 0.95) to outstanding (AUC=0.93, 95% CI=0.82, 0.97) for lifetime SUD. Regarding substance-specific diagnoses, the classification accuracy for current SUD diagnoses for alcohol (AUC=0.82) and cannabis (AUC=0.83) was excellent and for nicotine/tobacco was outstanding (AUC=0.90). For lifetime substance-specific diagnoses, the classification accuracy ranged from excellent for opioids (AUC=0.84) to outstanding for alcohol, cannabis, stimulant, sedative, and nicotine/tobacco (AUC=0.91 to 0.96). The median time to complete the CAT-SUD-E was 4 minutes 22 seconds compared to 45 minutes for the K-SADS. With administration time and burden similar to simple screeners and diagnostic accuracy comparable to a lengthy clinician-conducted interview, this study provides initial support for the CAT-SUD-E as a potentially feasible accurate diagnostic tool for assessing SUDs in adolescents. Future studies should further validate the CAT-SUD-E in a larger sample of adolescents, evaluate its use for measuring progress and change over time, and examine the acceptability, feasibility, and scalability of this assessment in youth-serving settings.