The fagerström test for nicotine dependence as a predictor of smoking abstinence: A pooled analysis of varenicline clinical trial data

Karl Fagerström, Cristina Russ, Ching Ray Yu, Carla Yunis, Jonathan Foulds

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142 Scopus citations

Abstract

Introduction: We explored the relationship between the Fagerström Test for Nicotine Dependence (FTND) and smoking abstinence rates in 10 randomized, double-blind placebo-controlled Phase 2-4 varenicline studies. Methods: Participants were adult smokers (≥10 cigarettes/day) who were motivated to quit. Efficacy end points included continuous abstinence rate (CAR) for weeks 9-24 analyzed, by baseline FTND and Heaviness of Smoking Index (HSI) scores, and treatment. Data were analyzed using logistic regression models. Results: Overall, 2,763 varenicline (M [SD] FTND score: 5.6 [2.2]) and 2,229 placebo subjects (5.5 [2.1]) were included in the analysis. An increase of one unit in baseline FTND or HSI score decreased the odds of abstinence at Week 24 by 11% (odds ratio [OR] 0.89, 95% CI 0.86-0.92, p < .0001) and 18% (OR 0.82, 95% CI 0.79-0.87, p < .0001), respectively. Treatment had a significant impact on CAR 9-24: odds of abstinence were increased threefold for varenicline versus placebo (OR 3.3, 95% CI 2.8-3.8, p < .0001). There was no interaction between treatment and FTND (p = .98) or HSI score (p = .97) for CAR 9-24. The HSI score predicted abstinence outcome as effectively as the FTND score. Conclusion: Abstinence rates decreased with increasing dependence scores. There was no interaction between treatment and baseline FTND or HSI score, suggesting that they have no effect on the efficacy of varenicline versus placebo. These results also suggest that the HSI may be as effective at predicting smoking cessation outcome as the whole FTND questionnaire.

Original languageEnglish (US)
Article numbernts018
Pages (from-to)1467-1473
Number of pages7
JournalNicotine and Tobacco Research
Volume14
Issue number12
DOIs
StatePublished - Dec 2012

All Science Journal Classification (ASJC) codes

  • General Medicine

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