Ward Ralph C, Tanner Nichole T, Silvestri Gerard A, Gebregziabher Mulugeta
Department of Public Health Sciences.
Division of Pulmonary, Critical Care and Sleep Medicine, Medical University of South Carolina, Charleston, SC.
JNCI Cancer Spectr. 2019 Apr 25;3(2):pkz014. doi: 10.1093/jncics/pkz014. eCollection 2019 Jun.
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.
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.
All LRT results were statistically significant ( ≤ .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.
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.
更强的尼古丁依赖与更高的肺癌发病率和肺癌死亡率相关。本研究调查将尼古丁依赖纳入肺癌发病率和死亡率风险预测模型是否能带来任何重要的临床益处。
使用了国家肺癌筛查试验美国放射学会成像网络组中14123名参与者的吸烟数据。我们在两个已发表的肺癌风险预测模型（Katki-Gu或PLCO-m2012）中添加尼古丁依赖作为主要暴露因素，并比较了四种结果：不采用烟草依赖测量指标、采用首次吸烟时间、采用吸烟强度指数以及采用尼古丁依赖的法格斯特龙测试。我们采用基于留一法的交叉验证方法，并使用似然比检验（LRT）、曲线下面积、一致性、1%和2%风险阈值下的敏感性和特异性以及净效益统计来比较性能。统计检验为双侧检验。
所有LRT结果均具有统计学意义（≤.0001），而其他检验结果则不然，不过特异性有统计学显著提高（P < .0001）由于LRT在检验预测增益方面渐近更具效力，我们得出结论，通过添加依赖测量指标，两个模型在统计水平上均得到了改进。其他性能统计结果总体表明，这种改进可能非常小净效益分析证实，纳入依赖测量指标并无明显的临床益处。
虽然将依赖测量指标添加到风险预测模型中可能不会带来临床益处，但尼古丁依赖测量指标仍应是患者咨询和鼓励戒烟的重要工具。
