Lara N Coughlin1,2, Allison N Tegge1,3, Christine E Sheffer4, Warren K Bickel1,2. 1. Addiction Recovery Research Center, Virginia Tech Carilion Research Institute, Roanoke, VA. 2. Department of Psychology, Virginia Tech, Blacksburg, VA. 3. Department of Statistics, Virginia Tech, Blacksburg, VA. 4. Department of Health Behavior, Roswell Park Comprehensive Cancer Center, Buffalo, NY.
Abstract
AIMS: Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches. METHODS: Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort. RESULTS: In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = -7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up. CONCLUSIONS: This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT. IMPLICATIONS: This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.
AIMS: Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches. METHODS: Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort. RESULTS: In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = -7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up. CONCLUSIONS: This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT. IMPLICATIONS: This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.
Authors: Christine Sheffer; James Mackillop; John McGeary; Reid Landes; Lawrence Carter; Richard Yi; Bryan Jones; Darren Christensen; Maxine Stitzer; Lisa Jackson; Warren Bickel Journal: Am J Addict Date: 2012-04-06
Authors: Christine E Sheffer; Darren R Christensen; Reid Landes; Larry P Carter; Lisa Jackson; Warren K Bickel Journal: Addict Behav Date: 2014-05-05 Impact factor: 3.913
Authors: Robert M Anthenelli; Neal L Benowitz; Robert West; Lisa St Aubin; Thomas McRae; David Lawrence; John Ascher; Cristina Russ; Alok Krishen; A Eden Evins Journal: Lancet Date: 2016-04-22 Impact factor: 79.321
Authors: Liqa N Athamneh; William B DeHart; Derek Pope; Alexandra M Mellis; Sarah E Snider; Brent A Kaplan; Warren K Bickel Journal: Psychol Addict Behav Date: 2019-03-21
Authors: Alina Shevorykin; Jami C Pittman; Warren K Bickel; Richard J O'Connor; Ria Malhotra; Neelam Prashad; Christine E Sheffer Journal: Health Behav Policy Rev Date: 2019-07
Authors: Cheng-Chien Lai; Wei-Hsin Huang; Betty Chia-Chen Chang; Lee-Ching Hwang Journal: Int J Environ Res Public Health Date: 2021-03-05 Impact factor: 3.390