摘要/ Abstract摘要:建立和验证长期吸烟者CT肺癌筛查风险模型

H. Katki, S. Kovalchik, C. Berg, L. Cheung, A. Chaturvedi
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引用次数: 2

摘要

美国预防服务工作组(USPSTF)建议对年龄在55-80岁、吸烟至少30包年、戒烟不超过15年的吸烟者进行计算机断层扫描(CT)肺癌筛查。然而,选择曾经吸烟的人进行筛查,使用个性化的肺癌风险计算可能比目前USPSTF的建议更有效和高效。我们比较了基于风险的CT肺部筛查策略和USPSTF推荐的模型结果。我们利用来自前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验(PLCO;1993-2009)对照组。协变量包括年龄、教育程度、性别、种族、吸烟强度/持续时间/戒烟年限、身体质量指数、肺癌家族史和自报肺气肿。模型在PLCO的胸片组和国家肺筛查试验(NLST;2002-2009年),并在全国健康访谈调查(NHIS;1997-2001),是美国的代表性样本。应用于美国50-80岁的吸烟者(NHIS 2010-2012)的模型来估计基于风险选择3年年度CT肺部筛查的结果,假设所有吸烟者的筛查产生NLST中观察到的肺癌检出率和死亡率的百分比变化。肺癌发病率和死亡风险模型在PLCO和NLST中得到了很好的校准。肺癌死亡模型对50-80岁的美国吸烟者进行了很好的校准和区分(NHIS 1997-2001:估计/观察=0.94,95%CI=0.84-1.05;AUC = 0.78, 95% ci -0.80 = 0.76)。根据USPSTF的建议,这些模型估计有900万美国吸烟者有资格进行肺癌筛查,估计有46,488例(95%CI=43,924-49,053)肺癌死亡在5年内是筛查可避免的(估计NNS=194, 95%CI=187-201)。相比之下,基于风险的选择筛查了相同数量的吸烟者(900万),5年肺癌风险最高(≥1.9%),估计可避免20%以上的死亡(55,717;95%CI=53,033-58,400),估计可使估计NNS降低17% (NNS=162, 95%CI=157-166)。在美国50-80岁的吸烟者队列中,与基于USPSTF建议的模型相比,应用基于风险的模型进行肺癌CT筛查估计与5年内预防的肺癌死亡人数更多以及预防1例肺癌死亡的较低NNS相关。引文格式:Hormuzd A. Katki, Stephanie A. Kovalchik, Christine D. Berg, Li C. Cheung, Anil K. Chaturvedi。发展和验证的风险模型,以选择曾经吸烟的CT肺癌筛查。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr IA18。
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Abstract IA18: Development and validation of risk models to select ever-smokers for CT lung-cancer screening
The US Preventive Services Task Force (USPSTF) recommends computed-tomography (CT) lung-cancer screening for ever-smokers ages 55-80 years who smoked at least 30 pack-years with no more than 15 years since quitting. However, selecting ever-smokers for screening using individualized lung-cancer risk calculations may be more effective and efficient than current USPSTF recommendations. We compare of modeled outcomes from risk-based CT lung-screening strategies versus USPSTF recommendations. We developed empirical risk models for lung-cancer incidence and death in the absence of CT screening using data on ever-smokers from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO; 1993-2009) control group. Covariates included age, education, sex, race, smoking intensity/duration/quit-years, Body Mass Index, family history of lung-cancer, and self-reported emphysema. Models were validated in the chest radiography groups of the PLCO and the National Lung Screening Trial (NLST; 2002-2009), with additional validation of the death model in the National Health Interview Survey (NHIS; 1997-2001), a representative sample of the US. Models applied to US ever-smokers ages 50-80 (NHIS 2010-2012) to estimate outcomes of risk-based selection for annual CT lung-screening for 3 years, assuming screening for all ever-smokers yields the percent changes in lung-cancer detection and death observed in the NLST. Lung-cancer incidence and death risk models were well-calibrated in PLCO and NLST. The lung-cancer death model calibrated and discriminated well for US ever-smokers ages 50-80 (NHIS 1997-2001: Estimated/Observed=0.94, 95%CI=0.84-1.05; AUC=0.78, 95%CI=0.76-0.80). Under USPSTF recommendations, the models estimated 9.0 million US ever-smokers would qualify for lung-cancer screening and 46,488 (95%CI=43,924-49,053) lung-cancer deaths were estimated as screen-avertable over 5 years (estimated NNS=194, 95%CI=187-201). In contrast, risk-based selection screened the same number of ever-smokers (9.0 million) at highest 5-year lung-cancer risk (≥1.9%), was estimated to avert 20% more deaths (55,717; 95%CI=53,033-58,400) and was estimated to reduce the estimated NNS by 17% (NNS=162, 95%CI=157-166). Among a cohort of US ever-smokers age 50-80 years, application of a risk-based model for CT screening for lung cancer compared with a model based on USPSTF recommendations was estimated to be associated with a greater number of lung-cancer deaths prevented over 5 years along with a lower NNS to prevent 1 lung-cancer death. Citation Format: Hormuzd A. Katki, Stephanie A. Kovalchik, Christine D. Berg, Li C. Cheung, Anil K. Chaturvedi. Development and validation of risk models to select ever-smokers for CT lung-cancer screening. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA18.
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