{"title":"Identifying treatment responders to the combination of varenicline and naltrexone.","authors":"Suzanna Donato, Lara A Ray","doi":"10.1111/ajad.70070","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objectives: </strong>The heterogeneity of alcohol and tobacco co-use suggests that only a subset of individuals will respond to a given pharmacotherapy. Toward identifying treatment responders, statistical learning was applied to a clinical trial combining naltrexone and varenicline for smoking cessation and drinking reduction.</p><p><strong>Method: </strong>Individuals (N = 165) who smoke cigarettes daily and drink alcohol heavily completed a Phase 2, double blind, randomized clinical trial comparing the efficacy of combination varenicline plus naltrexone versus varenicline plus placebo. Smoking cessation was defined by bio-verified nicotine abstinence. Drinking reduction was defined as a 2-level reduction in the World Health Organization (WHO) risk drinking level. Three statistical learning methods (ridge regression, LASSO regression, and random forest) were tested psychosocial and biological predictors of clinical response.</p><p><strong>Results: </strong>For drinking reduction, the LASSO regression had the highest overall accuracy (86%) and AUC (0.88). Important predictors included baseline alcohol consumption, baseline smoking urge, age of first cigarette use, and years of education. For nicotine abstinence, LASSO regression had the highest overall accuracy AUC (0.69). Important predictors included medication condition, expired alveolar CO level, baseline alcohol consumption, depression symptoms, and years of education.</p><p><strong>Conclusions: </strong>Baseline consumption patterns are a strong predictor of clinical outcome for both smoking cessation and drinking reduction. Results also underscore the important cross-relationship between drinking and smoking. Statistical learning models converged with previous hypothesis-driven studies and were well-suited for clinical trial datasets.</p><p><strong>Scientific significance: </strong>These findings highlight candidate variables that, with further validation, may support the development of personalized treatment strategies.</p>","PeriodicalId":7762,"journal":{"name":"American Journal on Addictions","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal on Addictions","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/ajad.70070","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SUBSTANCE ABUSE","Score":null,"Total":0}
引用次数: 0
Abstract
Background and objectives: The heterogeneity of alcohol and tobacco co-use suggests that only a subset of individuals will respond to a given pharmacotherapy. Toward identifying treatment responders, statistical learning was applied to a clinical trial combining naltrexone and varenicline for smoking cessation and drinking reduction.
Method: Individuals (N = 165) who smoke cigarettes daily and drink alcohol heavily completed a Phase 2, double blind, randomized clinical trial comparing the efficacy of combination varenicline plus naltrexone versus varenicline plus placebo. Smoking cessation was defined by bio-verified nicotine abstinence. Drinking reduction was defined as a 2-level reduction in the World Health Organization (WHO) risk drinking level. Three statistical learning methods (ridge regression, LASSO regression, and random forest) were tested psychosocial and biological predictors of clinical response.
Results: For drinking reduction, the LASSO regression had the highest overall accuracy (86%) and AUC (0.88). Important predictors included baseline alcohol consumption, baseline smoking urge, age of first cigarette use, and years of education. For nicotine abstinence, LASSO regression had the highest overall accuracy AUC (0.69). Important predictors included medication condition, expired alveolar CO level, baseline alcohol consumption, depression symptoms, and years of education.
Conclusions: Baseline consumption patterns are a strong predictor of clinical outcome for both smoking cessation and drinking reduction. Results also underscore the important cross-relationship between drinking and smoking. Statistical learning models converged with previous hypothesis-driven studies and were well-suited for clinical trial datasets.
Scientific significance: These findings highlight candidate variables that, with further validation, may support the development of personalized treatment strategies.
期刊介绍:
The American Journal on Addictions is the official journal of the American Academy of Addiction Psychiatry. The Academy encourages research on the etiology, prevention, identification, and treatment of substance abuse; thus, the journal provides a forum for the dissemination of information in the extensive field of addiction. Each issue of this publication covers a wide variety of topics ranging from codependence to genetics, epidemiology to dual diagnostics, etiology to neuroscience, and much more. Features of the journal, all written by experts in the field, include special overview articles, clinical or basic research papers, clinical updates, and book reviews within the area of addictions.