Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Lancet Respiratory Medicine,The - Tập 11 - Trang 685-697 - 2023
Weiqi Liao1, Carol A C Coupland1,2, Judith Burchardt1, David R Baldwin2,3
1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
2School of Medicine, University of Nottingham, Nottingham, UK
3Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK

Tài liệu tham khảo

Sung, 2021, Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin, 71, 209, 10.3322/caac.21660 Aberle, 2011, Reduced lung-cancer mortality with low-dose computed tomographic screening, N Engl J Med, 365, 395, 10.1056/NEJMoa1102873 de Koning, 2020, Reduced lung-cancer mortality with volume CT screening in a randomized trial, N Engl J Med, 382, 503, 10.1056/NEJMoa1911793 Moyer, 2014, Screening for lung cancer: US Preventive Services Task Force recommendation statement, Ann Intern Med, 160, 330 Krist, 2021, Screening for lung cancer: US Preventive Services Task Force recommendation statement, JAMA, 325, 962, 10.1001/jama.2021.1117 Herrett, 2015, Data resource profile: Clinical Practice Research Datalink (CPRD), Int J Epidemiol, 44, 827, 10.1093/ije/dyv098 Liao, 2022, Development, validation, and evaluation of prediction models to identify individuals at high risk of lung cancer for screening in the English primary care population using the QResearch database: research protocol and statistical analysis plan, medRxiv Collins, 2015, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement, Ann Intern Med, 162, 55, 10.7326/M14-0697 Moons, 2015, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration, Ann Intern Med, 162, W1, 10.7326/M14-0698 Hippisley-Cox, 2017, Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study, BMJ, 357 Hippisley-Cox, 2015, Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study, BMJ Open, 5 Rubin, 1987 Royston, 1999, The use of fractional polynomials to model continuous risk variables in epidemiology, Int J Epidemiol, 28, 964, 10.1093/ije/28.5.964 Toumazis, 2020, Risk-based lung cancer screening: a systematic review, Lung Cancer, 147, 154, 10.1016/j.lungcan.2020.07.007 Ten Haaf, 2017, Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study, PLoS Med, 14, 10.1371/journal.pmed.1002277 Robbins, 2021, Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom, Br J Cancer, 124, 2026, 10.1038/s41416-021-01278-0 Ten Haaf, 2015, Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials, Cancer Epidemiol Biomarkers Prev, 24, 154, 10.1158/1055-9965.EPI-14-0745 Cassidy, 2008, The LLP risk model: an individual risk prediction model for lung cancer, Br J Cancer, 98, 270, 10.1038/sj.bjc.6604158 Field, 2021, Liverpool Lung Project lung cancer risk stratification model: calibration and prospective validation, Thorax, 76, 161, 10.1136/thoraxjnl-2020-215158 Katki, 2016, Development and validation of risk models to select ever-smokers for CT lung cancer screening, JAMA, 315, 2300, 10.1001/jama.2016.6255 Tammemägi, 2013, Selection criteria for lung-cancer screening, N Engl J Med, 368, 728, 10.1056/NEJMoa1211776 Tammemägi, 2014, Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts, PLoS Med, 11, 10.1371/journal.pmed.1001764 Wilson, 2015, A simple model for predicting lung cancer occurrence in a lung cancer screening program: the Pittsburgh Predictor, Lung Cancer, 89, 31, 10.1016/j.lungcan.2015.03.021 Bach, 2003, Variations in lung cancer risk among smokers, J Natl Cancer Inst, 95, 470, 10.1093/jnci/95.6.470 Newson, 2010, Comparing the predictive powers of survival models using Harrell's C or Somers' D, Stata J, 10, 339, 10.1177/1536867X1001000303 Royston, 2004, A new measure of prognostic separation in survival data, Stat Med, 23, 723, 10.1002/sim.1621 Royston, 2006, Explained variation for survival models, Stata J, 6, 1, 10.1177/1536867X0600600105 Ensor, 2018 Vickers, 2019, A simple, step-by-step guide to interpreting decision curve analysis, Diagn Progn Res, 3, 18, 10.1186/s41512-019-0064-7 Hippisley-Cox, 2021, Predicting the risk of prostate cancer in asymptomatic men: a cohort study to develop and validate a novel algorithm, Br J Gen Pract, 71, e364, 10.3399/bjgp20X714137 Goldstein, 2017, Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review, J Am Med Inform Assoc, 24, 198, 10.1093/jamia/ocw042 O'Dowd, 2022, Selection of eligible participants for screening for lung cancer using primary care data, Thorax, 77, 882, 10.1136/thoraxjnl-2021-217142 Ten Haaf, 2021, Personalising lung cancer screening: an overview of risk-stratification opportunities and challenges, Int J Cancer, 149, 250, 10.1002/ijc.33578