• Georgetown University Medical Center, Washington, DC 20007, USA.

BACKGROUND: The impact of lung cancer screening on smoking behavior is unclear. The aims of this ancillary study of the Prostate Lung Colorectal and Ovarian Cancer Screening Trial were to produce risk prediction models to identify individuals at risk of relapse or continued smoking and to evaluate whether cancer-screening variables affect long-term smoking outcomes.

METHODS: Participants completed a baseline questionnaire at trial enrollment and a supplemental questionnaire 4-14 years after enrollment, which assessed several cancer-related variables, including family history of cancer, comorbidities, and tobacco use. Multivariable logistic regression models were used to predict smoking status at completion of the supplemental questionnaire. The models' predictive performances were evaluated by assessing discrimination via the receiver operator characteristic area under the curve (ROC AUC) and calibration. Models were internally validated using bootstrap methods.

RESULTS: Of the 31 694 former smokers on the baseline questionnaire, 1042 (3.3%) had relapsed (ie, reported being a current smoker on the supplemental questionnaire). Of the 6807 current smokers on the baseline questionnaire, 4439 (65.2%) reported continued smoking on the supplemental questionnaire. Relapse was associated with multiple demographic, medical, and tobacco-related characteristics. This model had a bootstrap median ROC AUC of 0.862 (95% confidence interval [CI] = 0.858 to 0.866) and a calibration slope of 1.004 (95% CI = 0.978 to 1.029), indicating excellent discrimination and calibration. Predictors of continued smoking also included multiple demographic, medical, and tobacco-related characteristics. This model had an ROC AUC of 0.611 (95% CI = 0.605 to 0.614) and a slope of 1.006 (95% CI = 0.962 to 1.041), indicating modest discrimination. Neither the trial arm nor the lung-screening result was statistically significantly associated with smoking outcomes.

CONCLUSION: These models, if validated externally, may have public health utility in identifying individuals at risk for adverse smoking outcomes, who may benefit from relapse prevention and smoking cessation interventions.