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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)...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
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.
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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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