title: Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk.
abstract: BACKGROUND
  Clinical guidelines for breast cancer chemoprevention and MRI screening involve estimates of remaining lifetime risk (RLR); in the United States, women with an RLR of 20% or higher meet "high-risk" criteria for MRI screening.
  
  
  METHODS
  We prospectively followed 1764 women without breast cancer to compare the RLRs and 10-year risks assigned by the risk models International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and to compare both sets of model-assigned 10-year risks to subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. All statistical tests are two-sided.
  
  
  RESULTS
  The models classified different proportions of women as high-risk (IBIS = 59.3% vs BOADICEA = 20.1%) using the RLR threshold of 20%. The difference was smaller (IBIS = 52.9% vs BOADICEA = 43.2%) using a 10-year risk threshold of 3.34%. IBIS risks (mean = 4.9%) were better calibrated to observed breast cancer incidence (5.2%, 95% confidence interval (CI) = 4.2% to 6.4%) than were those of BOADICEA (mean = 3.7%) overall and within quartiles of model risk (P = .20 by IBIS and P = .07 by BOADICEA). Both models gave similar discrimination, with AUCs of 0.67 (95% CI = 0.61 to 0.73) using IBIS and 0.68 (95% CI = 0.62 to 0.74) using BOADICEA. Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA.
  
  
  CONCLUSIONS
  RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models. Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years).
authors.affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT).
authors.forename: Anne S
authors.initials: AS
authors.lastname: Quante
authors.affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT).
authors.forename: Alice S
authors.initials: AS
authors.lastname: Whittemore
authors.affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT).
authors.forename: Tom
authors.initials: T
authors.lastname: Shriver
authors.affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT).
authors.forename: John L
authors.initials: JL
authors.lastname: Hopper
authors.affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT).
authors.forename: Konstantin
authors.initials: K
authors.lastname: Strauch
authors.affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT). mt146@columbia.edu.
authors.forename: Mary Beth
authors.initials: MB
authors.lastname: Terry
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D019313:BRCA1 Protein / Q000235:genetics; D024682:BRCA2 Protein / Q000235:genetics; D001943:Breast Neoplasms / Q000453:epidemiology* / Q000235:genetics / Q000517:prevention & control*; D005260:Female; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000592:standards; D006579:Heterozygote; D006801:Humans; D015994:Incidence; D053208:Kaplan-Meier Estimate; D008017:Life Expectancy; D008875:Middle Aged; D015233:Models, Statistical; D009154:Mutation; D016017:Odds Ratio; D017410:Practice Guidelines as Topic; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology
chemical_list: D019313:BRCA1 Protein; C492913:BRCA1 protein, human; D024682:BRCA2 Protein; C551750:BRCA2 protein, human