Olaide Y. Raji, PhD; Stephen W. Duffy, MSc; Olorunshola F. Agbaje, PhD; Stuart G. Baker, ScD; David C. Christiani, MD, MPH; Adrian Cassidy, PhD; John K. Field, PhD, FRCPath
Acknowledgment: The authors thank all study participants, the EUELC Consortium, Dr. Andrew J. Vickers for his useful discussion and helpful comments during the statistical data analysis and preparation of the manuscript, and Professor Anne Field for reading the manuscript as a nonexpert clinician. For members of the EUELC Consortium, see the Appendix.
Grant Support: By the Roy Castle Lung Cancer Foundation, the National Institute for Health Research Health Technology Assessment program, and the American Cancer Society, as well as grants CA74386, CA092824, and CA090578 from the National Cancer Institute, National Institutes of Health (Dr. Christiani).
Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-1994.
Reproducible Research Statement:Study protocol: Available from Professor Field (e-mail, J.K.Field@liv.ac.uk). Statistical code: Available from Dr. Raji (e-mail, O.Y.Raji@liv.ac.uk). Relative utility curves are available from Dr. Baker (e-mail, email@example.com). Data set: LLCC and LLPC data are available from Professor Field (e-mail, J.K.Field@liv.ac.uk) on completion of a data transfer agreement. Harvard and EUELC data need separate approval for their release.
Requests for Single Reprints: John K. Field, PhD, FRCPath, Roy Castle Lung Cancer Research Programme, The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool L3 9TA, United Kingdom; e-mail, J.K.Field@liv.ac.uk.
Current Author Addresses: Dr. Raji and Professor Field: Roy Castle Lung Cancer Research Programme, The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool L3 9TA, United Kingdom.
Professor Duffy: Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, United Kingdom.
Dr. Agbaje: Division of Cancer Studies, Cancer Epidemiology Unit, King's College London, School of Medicine, Academic Oncology, 3rd Floor, Bermondsey Wing, Guy's Hospital, London SE1 9RT, United Kingdom.
Dr. Baker: National Cancer Institute, EPN 3131, 6130 Executive Boulevard MSC, Bethesda, MD 20892.
Dr. Christiani: Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115.
Dr. Cassidy: GlaxoSmithKline Vaccines, Parc de la Noire Epine, Rue Fleming 20, 1300 Wavre, Belgium.
Author Contributions: Conception and design: S.W. Duffy, A. Cassidy, J.K. Field.
Analysis and interpretation of the data: O.Y. Raji, S.W. Duffy, O.F. Agbaje, S.G. Baker, D.C. Christiani, A. Cassidy, J.K. Field.
Drafting of the article: O.Y. Raji, O.F. Agbaje, A. Cassidy, J.K. Field.
Critical revision of the article for important intellectual content: O.Y. Raji, S.G. Baker, D.C. Christiani, A. Cassidy, J.K. Field.
Final approval of the article: O.Y. Raji, S.W. Duffy, O.F. Agbaje, D.C. Christiani, A. Cassidy, J.K. Field.
Provision of study materials or patients: D.C. Christiani, J.K. Field.
Statistical expertise: S.W. Duffy, S.G. Baker.
Obtaining of funding: D.C. Christiani, J.K. Field.
Administrative, technical, or logistic support: D.C. Christiani, J.K. Field.
Collection and assembly of data: O.Y. Raji, A. Cassidy, J.K. Field.
Raji OY, Duffy SW, Agbaje OF, Baker SG, Christiani DC, Cassidy A, et al. Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer: A Case–Control and Cohort Validation Study. Ann Intern Med. 2012;157:242-250. doi: 10.7326/0003-4819-157-4-201208210-00004
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Published: Ann Intern Med. 2012;157(4):242-250.
External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit.
To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America.
Case–control and prospective cohort study.
Europe and North America.
Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study.
5-year absolute risks for lung cancer predicted by the LLP model.
The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening.
The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model.
Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening.
Roy Castle Lung Cancer Foundation.
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Cancer Screening/Prevention, Hematology/Oncology, Lung Cancer, Prevention/Screening, Pulmonary/Critical Care.
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