The American journal on addictions · 2026
Identifying clinical correlates of drinking clusters during treatment for alcohol use disorder.
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Kohler Robert J, Zhou Hang, Zakiniaeiz Yasmin, Verplaetse Terril L, Garcia-Rivas Vernon, Peltier MacKenzie R, Banini Bubu A, McKee Sherry A, Roberts Walter
Аннотация
BACKGROUND AND OBJECTIVES: Despite the availability of treatments for alcohol use disorder (AUD), relapse prevalence and health-related consequences associated with AUD remains high. Using data-driven approaches that enhance generalizability can help elucidate relationships between treatment outcomes and alcohol consumption, aiding in the discovery of novel treatment targets for AUD subtypes.
METHODS: We merged data (n = 2045) across four Phase 2 randomized clinical trials affiliated with the NIAAA Clinical Investigations Group and a Phase 3 trial (NIAAA Sponsored). Participants were clustered based on self-reported drinking during treatment maintenance. A gradient boosted machine learning model with end-of-treatment clinical features was used to predict the clusters we identified.
RESULTS: We identified a three-cluster solution corresponding to low (MStandard Drinking Units (SDU) = 1.68, n = 1677), moderate (MSDU = 6.70, n = 253), and high (MSDU = 12.92, n = 115) clusters of alcohol consumption during treatment maintenance. We achieved modest prediction of the clusters (AccuracyTrain = 71.0%; AUCTrain = 0.79) using demographics and end-of-treatment clinical and biological assessments. Between-cluster differences were observed between low and high clusters on measures of depression and anxiety (MDifference = 0.49, SE = 0.13, p = .004), drinking consequences (MDifference = 1.02, SE = 0.13, p < .001) and liver functioning (0.39 ≤ MDifference ≤ 0.52, 0.12 ≤ SE ≤ 0.13, 0.001 ≤ p ≤ .005).
DISCUSSION AND CONCLUSIONS: These findings suggest that generalizable clusters of alcohol consumption exist across these clinical trials characterized by core demographics, clinical, and biological phenotypes, irrespective of the treatment received. We further show that some assessments may not be useful in distinguishing between higher levels of consumption.
SCIENTIFIC SIGNIFICANCE: Identifying predictive features of AUD subtypes, across different phases of treatment, can assist clinicians in identifying individuals who require additional support.
METHODS: We merged data (n = 2045) across four Phase 2 randomized clinical trials affiliated with the NIAAA Clinical Investigations Group and a Phase 3 trial (NIAAA Sponsored). Participants were clustered based on self-reported drinking during treatment maintenance. A gradient boosted machine learning model with end-of-treatment clinical features was used to predict the clusters we identified.
RESULTS: We identified a three-cluster solution corresponding to low (MStandard Drinking Units (SDU) = 1.68, n = 1677), moderate (MSDU = 6.70, n = 253), and high (MSDU = 12.92, n = 115) clusters of alcohol consumption during treatment maintenance. We achieved modest prediction of the clusters (AccuracyTrain = 71.0%; AUCTrain = 0.79) using demographics and end-of-treatment clinical and biological assessments. Between-cluster differences were observed between low and high clusters on measures of depression and anxiety (MDifference = 0.49, SE = 0.13, p = .004), drinking consequences (MDifference = 1.02, SE = 0.13, p < .001) and liver functioning (0.39 ≤ MDifference ≤ 0.52, 0.12 ≤ SE ≤ 0.13, 0.001 ≤ p ≤ .005).
DISCUSSION AND CONCLUSIONS: These findings suggest that generalizable clusters of alcohol consumption exist across these clinical trials characterized by core demographics, clinical, and biological phenotypes, irrespective of the treatment received. We further show that some assessments may not be useful in distinguishing between higher levels of consumption.
SCIENTIFIC SIGNIFICANCE: Identifying predictive features of AUD subtypes, across different phases of treatment, can assist clinicians in identifying individuals who require additional support.