In selecting ever-smokers for computed tomography lung cancer screening, 4 risk prediction models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best, a recent study found. The study compared the US screening populations selected by 9 lung cancer risk models and examined their predictive performance in 2 cohorts. Model performance was evaluated using data from 337,388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72,338 ever-smokers in the Cancer Prevention Study II (CPS-II) Nutrition Survey cohort. Researchers found:
- At a 5-year risk threshold of 2.0%, the models chose US screening populations ranging from 7.6 million to 26 million ever-smokers.
- 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated and had higher area under the curve (AUC) than 5 models that generally overestimated risk and had lower AUCs.
- The 4 models had the highest sensitivity at a fixed specialty and similar discrimination at a fixed risk threshold.
- The 4 models also showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons screened.
Katki HA, Kovalchik SA, Petito LC, et al. Implications of nine risk prediction models for selecting ever-smokers for computed tomography lung cancer screening. [Published online ahead of print May 15, 2018]. Ann Intern Med. doi:10.7326/M17-2701.
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