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Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening.


ABSTRACT: IMPORTANCE:The US Preventive Services Task Force (USPSTF) recommends computed tomography (CT) lung cancer screening for ever-smokers aged 55 to 80 years who have 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. OBJECTIVE:Comparison of modeled outcomes from risk-based CT lung-screening strategies vs USPSTF recommendations. DESIGN, SETTING, AND PARTICIPANTS: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, and quit-years; body mass index; family history of lung cancer; and self-reported emphysema. Model validation 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 United States. Models were applied to US ever-smokers aged 50 to 80 years (NHIS 2010-2012) to estimate outcomes of risk-based selection for CT lung screening, assuming screening for all ever-smokers, yield the percent changes in lung cancer detection and death observed in the NLST. EXPOSURES:Annual CT lung screening for 3 years beginning at age 50 years. MAIN OUTCOMES AND MEASURES:For model validity: calibration (number of model-predicted cases divided by number of observed cases [estimated/observed]) and discrimination (area under curve [AUC]). For modeled screening outcomes: estimated number of screen-avertable lung cancer deaths and estimated screening effectiveness (number needed to screen [NNS] to prevent 1 lung cancer death). RESULTS: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 aged 50 to 80 years (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 screening of 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]). CONCLUSIONS AND RELEVANCE:Among a cohort of US ever-smokers aged 50 to 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.

SUBMITTER: Katki HA 

PROVIDER: S-EPMC4899131 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening.

Katki Hormuzd A HA   Kovalchik Stephanie A SA   Berg Christine D CD   Cheung Li C LC   Chaturvedi Anil K AK  

JAMA 20160601 21


<h4>Importance</h4>The US Preventive Services Task Force (USPSTF) recommends computed tomography (CT) lung cancer screening for ever-smokers aged 55 to 80 years who have 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.<h4>Objective</h4>Comparison of modeled outcomes from risk-based CT lung-screening strat  ...[more]

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