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A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies
BACKGROUND: Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF)...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225672/ https://www.ncbi.nlm.nih.gov/pubmed/31566216 http://dx.doi.org/10.1093/jnci/djz164 |
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author | ten Haaf, Kevin Bastani, Mehrad Cao, Pianpian Jeon, Jihyoun Toumazis, Iakovos Han, Summer S Plevritis, Sylvia K Blom, Erik F Kong, Chung Yin Tammemägi, Martin C Feuer, Eric J Meza, Rafael de Koning, Harry J |
author_facet | ten Haaf, Kevin Bastani, Mehrad Cao, Pianpian Jeon, Jihyoun Toumazis, Iakovos Han, Summer S Plevritis, Sylvia K Blom, Erik F Kong, Chung Yin Tammemägi, Martin C Feuer, Eric J Meza, Rafael de Koning, Harry J |
author_sort | ten Haaf, Kevin |
collection | PubMed |
description | BACKGROUND: Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations. METHODS: Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis. RESULTS: Risk-based screening strategies requiring similar screens among individuals ages 55–80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%. CONCLUSIONS: Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages. |
format | Online Article Text |
id | pubmed-7225672 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-72256722020-05-19 A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies ten Haaf, Kevin Bastani, Mehrad Cao, Pianpian Jeon, Jihyoun Toumazis, Iakovos Han, Summer S Plevritis, Sylvia K Blom, Erik F Kong, Chung Yin Tammemägi, Martin C Feuer, Eric J Meza, Rafael de Koning, Harry J J Natl Cancer Inst Articles BACKGROUND: Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations. METHODS: Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis. RESULTS: Risk-based screening strategies requiring similar screens among individuals ages 55–80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%. CONCLUSIONS: Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages. Oxford University Press 2019-11-29 /pmc/articles/PMC7225672/ /pubmed/31566216 http://dx.doi.org/10.1093/jnci/djz164 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. |
spellingShingle | Articles ten Haaf, Kevin Bastani, Mehrad Cao, Pianpian Jeon, Jihyoun Toumazis, Iakovos Han, Summer S Plevritis, Sylvia K Blom, Erik F Kong, Chung Yin Tammemägi, Martin C Feuer, Eric J Meza, Rafael de Koning, Harry J A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies |
title | A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies |
title_full | A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies |
title_fullStr | A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies |
title_full_unstemmed | A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies |
title_short | A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies |
title_sort | comparative modeling analysis of risk-based lung cancer screening strategies |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225672/ https://www.ncbi.nlm.nih.gov/pubmed/31566216 http://dx.doi.org/10.1093/jnci/djz164 |
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