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Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial

IMPORTANCE: Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals. OBJECTIVES: To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results. DESIGN, SETTING, AND PARTICIPANTS: A logistic regressi...

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Autores principales: Tammemägi, Martin C., ten Haaf, Kevin, Toumazis, Iakovos, Kong, Chung Yin, Han, Summer S., Jeon, Jihyoun, Commins, John, Riley, Thomas, Meza, Rafael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484623/
https://www.ncbi.nlm.nih.gov/pubmed/30821827
http://dx.doi.org/10.1001/jamanetworkopen.2019.0204
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author Tammemägi, Martin C.
ten Haaf, Kevin
Toumazis, Iakovos
Kong, Chung Yin
Han, Summer S.
Jeon, Jihyoun
Commins, John
Riley, Thomas
Meza, Rafael
author_facet Tammemägi, Martin C.
ten Haaf, Kevin
Toumazis, Iakovos
Kong, Chung Yin
Han, Summer S.
Jeon, Jihyoun
Commins, John
Riley, Thomas
Meza, Rafael
author_sort Tammemägi, Martin C.
collection PubMed
description IMPORTANCE: Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals. OBJECTIVES: To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results. DESIGN, SETTING, AND PARTICIPANTS: A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n = 14 576) and ACRIN (n = 7653) participants who had 3 screens, adequate follow-up, and complete predictor information. MAIN OUTCOMES AND MEASURES: Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results. RESULTS: Overall, the mean (SD) age of 22 229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22 229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P < .001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative. CONCLUSIONS AND RELEVANCE: According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00047385
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spelling pubmed-64846232019-05-21 Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial Tammemägi, Martin C. ten Haaf, Kevin Toumazis, Iakovos Kong, Chung Yin Han, Summer S. Jeon, Jihyoun Commins, John Riley, Thomas Meza, Rafael JAMA Netw Open Original Investigation IMPORTANCE: Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals. OBJECTIVES: To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results. DESIGN, SETTING, AND PARTICIPANTS: A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n = 14 576) and ACRIN (n = 7653) participants who had 3 screens, adequate follow-up, and complete predictor information. MAIN OUTCOMES AND MEASURES: Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results. RESULTS: Overall, the mean (SD) age of 22 229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22 229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P < .001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative. CONCLUSIONS AND RELEVANCE: According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00047385 American Medical Association 2019-03-01 /pmc/articles/PMC6484623/ /pubmed/30821827 http://dx.doi.org/10.1001/jamanetworkopen.2019.0204 Text en Copyright 2019 Tammemägi MC et al. JAMA Network Open. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Tammemägi, Martin C.
ten Haaf, Kevin
Toumazis, Iakovos
Kong, Chung Yin
Han, Summer S.
Jeon, Jihyoun
Commins, John
Riley, Thomas
Meza, Rafael
Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial
title Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial
title_full Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial
title_fullStr Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial
title_full_unstemmed Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial
title_short Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial
title_sort development and validation of a multivariable lung cancer risk prediction model that includes low-dose computed tomography screening results: a secondary analysis of data from the national lung screening trial
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484623/
https://www.ncbi.nlm.nih.gov/pubmed/30821827
http://dx.doi.org/10.1001/jamanetworkopen.2019.0204
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