Patterns of drinking behavior around a treatment episode for alcohol use disorder: Predictions from pre-treatment...

Alcohol use disorder (AUD) has been described as a chronic disease given the high rates that affected individuals have in returning to drinking after a change attempt. Many studies have characterized predictors of aggregated alcohol use (e.g., percent heavy drinking days) following treatment for AUD. However, to inform future research on predicting drinking as an AUD outcome measure, a better understanding is needed of the patterns of drinking that surround a treatment episode and which clinical measures predict patterns of drinking.

We analyzed data from the Project MATCH and COMBINE studies (MATCH: n = 1726; 24.3% female, 20.0% non-White; COMBINE: n = 1383; 30.9% female, 23.2% non-White). Daily drinking was measured in the 90 days prior to treatment, 90 days (MATCH) and 120 days (COMBINE) during treatment, and 365 days following treatment. Gradient boosting machine learning methods were used to explore baseline predictors of drinking patterns.

Drinking patterns during a prior time period were the most consistent predictors of future drinking patterns. Social network drinking, AUD severity, mental health symptoms, and constructs based on the addiction cycle (incentive salience, negative emotionality, and executive function) were associated with patterns of drinking prior to treatment. Addiction cycle constructs, AUD severity, purpose in life, social network, legal history, craving, and motivation were associated with drinking during the treatment period and following treatment.

There is heterogeneity in drinking patterns around an AUD treatment episode. This study provides novel information about variables that may be important to measure to improve the prediction of drinking patterns during and following treatment. Future research should consider which patterns of drinking they aim to predict and which period of drinking is most important to predict. The current findings could guide the selection of predictor variables and generate hypotheses for those predictors.