Schonberg Mara A, Li Vicky W, Eliassen A Heather, Davis Roger B, LaCroix Andrea Z, McCarthy Ellen P, Rosner Bernard A, Chlebowski Rowan T, Hankinson Susan E, Marcantonio Edward R, Ngo Long H
Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Beth Israel Deaconess Medical Center, 1309 Beacon, Office 219, Brookline, MA, 02446, USA.
Breast Cancer Res Treat. 2016 Dec;160(3):547-562. doi: 10.1007/s10549-016-4020-8. Epub 2016 Oct 21.
Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death.
We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years).
Within 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]).
We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.
准确的风险评估对于乳腺癌预防决策至关重要。我们旨在开发一种针对绝经后女性的乳腺癌预测模型,该模型将考虑她们个体的非乳腺癌死亡竞争风险。
我们纳入了73066名完成2004年护士健康研究(NHS)问卷的女性(均≥57岁),并对参与者进行随访至2014年5月。我们考虑了17个乳腺癌风险因素(健康行为、人口统计学、家族史、生殖因素)以及7个非乳腺癌死亡风险因素(合并症、功能依赖)和乳腺X线摄影使用情况。我们使用竞争风险回归来识别与乳腺癌独立相关的因素。我们通过使用来自妇女健康倡议扩展研究(WHI-ES;均≥55岁且随访5年)的74887名受试者来检验校准(乳腺癌发病率的预期与观察比值,E/O)和辨别力(c统计量),从而验证最终模型。
在5年内,NHS参与者中有1.8%被诊断患有乳腺癌(WHI-ES中为2.0%,p = 0.02),6.6%经历了非乳腺癌死亡(WHI-ES中为5.2%,p < 0.001)。使用结合了赤池信息准则、c统计量、统计学显著性和临床判断的模型选择程序,我们的最终模型包括9个乳腺癌风险因素、5种合并症、功能依赖和乳腺X线摄影使用情况。该模型在NHS中的c统计量为0.61(95%CI[0.60 - 0.63]),在WHI-ES中为0.57(0.55 - 0.58)。平均而言,我们的模型在WHI-ES中对乳腺癌的预测偏低(E/O为0.92[0.88 - 0.97])。
我们开发了一种新颖的预测模型,在估计乳腺癌风险时考虑了绝经后女性个体的非乳腺癌死亡竞争风险。