Steven F Babbin1, Catherine Stanger2, Emily A Scherer3, Alan J Budney2. 1. Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA. Electronic address: steven.f.babbin@dartmouth.edu. 2. Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA. 3. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA; Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Abstract
INTRODUCTION: Outpatient treatments for adolescent substance use demonstrate clinically meaningful reductions in substance use, but effect sizes are often low, relapse rates are high, and response to treatment is heterogeneous across participants. The present study utilized cluster analysis to identify subgroups of treatment response among adolescents from three randomized clinical trials evaluating behavioral treatments for substance use. METHODS: Analyses were performed on a sample of 194 adolescents (average age=15.8, 81.4% male) who reported cannabis use during the past 30days or had a cannabis-positive urine test. Clustering was based on percent days cannabis use at 5 time periods (intake, end of treatment, 3, 6, and 9months post-treatment). Participants in the identified subgroups were then compared across a number of variables not involved in the clustering (e.g., substance use, demographics, and psychopathology) to test for predictors of cluster membership. RESULTS: Four clusters were identified based on statistical indices and visual inspection of the resulting cluster profiles: Low Use Responders (n=109, low baseline level, sustained decrease); High Use Responders (n=45, high baseline level, sustained decrease); Relapsers (n=25, medium baseline level, decrease, rapid increase post-treatment); and Non-Responders (n=15; consistently high level of use). Cannabis dependence, mean cannabis uses per day, and socioeconomic status were predictive of cluster membership. CONCLUSIONS: Cluster analysis empirically identified different patterns of treatment response over time for adolescent outpatients. Investigating homogenous subgroups of participants provides insight into study outcomes, and variables associated with clusters have potential utility to identify participants that may benefit from more intensive treatment.
RCT Entities:
INTRODUCTION:Outpatient treatments for adolescent substance use demonstrate clinically meaningful reductions in substance use, but effect sizes are often low, relapse rates are high, and response to treatment is heterogeneous across participants. The present study utilized cluster analysis to identify subgroups of treatment response among adolescents from three randomized clinical trials evaluating behavioral treatments for substance use. METHODS: Analyses were performed on a sample of 194 adolescents (average age=15.8, 81.4% male) who reported cannabis use during the past 30days or had a cannabis-positive urine test. Clustering was based on percent days cannabis use at 5 time periods (intake, end of treatment, 3, 6, and 9months post-treatment). Participants in the identified subgroups were then compared across a number of variables not involved in the clustering (e.g., substance use, demographics, and psychopathology) to test for predictors of cluster membership. RESULTS: Four clusters were identified based on statistical indices and visual inspection of the resulting cluster profiles: Low Use Responders (n=109, low baseline level, sustained decrease); High Use Responders (n=45, high baseline level, sustained decrease); Relapsers (n=25, medium baseline level, decrease, rapid increase post-treatment); and Non-Responders (n=15; consistently high level of use). Cannabis dependence, mean cannabis uses per day, and socioeconomic status were predictive of cluster membership. CONCLUSIONS: Cluster analysis empirically identified different patterns of treatment response over time for adolescent outpatients. Investigating homogenous subgroups of participants provides insight into study outcomes, and variables associated with clusters have potential utility to identify participants that may benefit from more intensive treatment.
Authors: Nora Penzel; Rachele Sanfelici; Linda A Antonucci; Linda T Betz; Dominic Dwyer; Anne Ruef; Kang Ik K Cho; Paul Cumming; Oliver Pogarell; Oliver Howes; Peter Falkai; Rachel Upthegrove; Stefan Borgwardt; Paolo Brambilla; Rebekka Lencer; Eva Meisenzahl; Frauke Schultze-Lutter; Marlene Rosen; Theresa Lichtenstein; Lana Kambeitz-Ilankovic; Stephan Ruhrmann; Raimo K R Salokangas; Christos Pantelis; Stephen J Wood; Boris B Quednow; Giulio Pergola; Alessandro Bertolino; Nikolaos Koutsouleris; Joseph Kambeitz Journal: Schizophrenia (Heidelb) Date: 2022-03-09