Impact of Tobacco Dependence in Risk Prediction Models for Lung Cancer Diagnoses and Deaths

BACKGROUND: Stronger nicotine dependence is associated with greater lung cancer incidence and lung cancer death. This study investigates whether including nicotine dependence in risk prediction models for lung cancer incidence and mortality provides any important clinical benefits. METHODS: Smoking data were used from 14 123 participants in the American College of Radiology Imaging Network arm of the National Lung Screening trial. We added nicotine dependence as the primary exposure in two published lung cancer risk prediction models (Katki-Gu or PLCO-m2012) and compared four results: with no tobacco-dependence measure, with time to first cigarette, with heaviness of smoking index, and with Fagestrom test for nicotine dependence. We used a cross-validation method based on leave-one-out and compared performance using likelihood ratio tests (LRT), area under the curve, concordance, sensitivity and specificity for 1% and 2% risk thresholds, and net benefit statistics. Statistical tests were two-sided. RESULTS: All LRT results were statistically significant (P ≤ .0001), whereas other tests were not, except that specificity statistically significantly improved (P < .0001). Because the LRT is asymptotically more powerful for testing for prediction gain, we conclude that both models were improved on a statistical level by adding dependence measures. The other performance statistics generally indicated that such gains were likely very small. Net benefit analysis confirmed there was no apparent clinical benefit for including dependence measures. CONCLUSIONS: Although inclusion of dependence measures may not provide a clinical benefit when added to risk prediction models, nicotine-dependence measures should nonetheless be an integral tool for patient counseling and for encouraging tobacco cessation.