A prediction model to simultaneously estimate personal risk of breast cancer and death from other causes in women aged 55 and older

一种同时估计 55 岁及以上女性患乳腺癌和其他原因死亡的个人风险的预测模型

• 批准号: 10223246
• 负责人: MARA A SCHONBERG
• 金额: $ 54.74万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10223246
• 关键词: Address; Age; Area; Breast Cancer Detection; Breast Cancer Risk Assessment Tool; Breast Cancer Risk Factor; Caring; Cause of Death; Cessation of life; Communities; Data; Detection; Development; Discrimination; Effectiveness; Ensure; Ethnic Origin; Family; General Population; Guidelines; Harm Reduction; Health; Health behavior; High Risk Woman; Incidence; Individual; Internet; Life Expectancy; Mammographic screening; Mammography; Modeling; Not Hispanic or Latino; Nurses' Health Study; Odds Ratio; Outcome; Participant; Performance; Population; Postmenopause; Primary Health Care; Quality of life; Race; Randomized Controlled Trials; Recording of previous events; Reproductive History; Risk; Risk Factors; Study models; Testing; Validation; Woman; Women' s Health; aged; base; black women; breast density; cancer diagnosis; care providers; clinical practice; cohort; experience; genomic data; hazard; high risk; improved; malignant breast neoplasm; mortality; mortality risk; multi-ethnic; novel; older driver; older women; personalized approach; population based; predictive modeling; preference; response; risk prediction model; risk stratification; screening; tumor; user-friendly; web page; web site

The incidence of breast cancer increases with age. While mammography screening reduces breast cancer mortality in women 40-74 years its efficacy in women >75 years in not known and on average it takes 10.7 years before 1 in 1,000 women avoids breast cancer death as a result of being screened. There are also risks to being screened, which include overdiagnosis (detection of non-lethal tumors). Thus, guidelines recommend that clinicians consider older women's breast cancer risk and life expectancy when deciding on screening. Yet, clinicians find it difficult to assess how an older woman's breast cancer risk and health interact to determine whether the potential benefits of screening outweigh the risks. Likely because existing models were not developed for use with older women and no model simultaneously predicts breast cancer and non-breast cancer (BC) death to help inform these decisions. To improve breast cancer prediction in older women, we previously developed a novel model to predict 5-year breast cancer risk among postmenopausal women >55 years using competing risk regression (CRR) and data from the Nurses' Health Study (NHS). We then examined our model's performance among Women's Health Initiative (WHI) participants. Our model considers age, family history, health behaviors, reproductive factors, and health in assessing breast cancer risk. We found that our model accurately predicted breast cancer in women 55-74 but underpredicted breast cancer in women >75 in WHI. Our model's discrimination was similar to that of the Breast Cancer Risk Assessment Tool (BCRAT, the most common risk model used in primary care) in WHI but our model accurately risk-stratified more older women than BCRAT. Before implementing our model, we aim to extend it to predict 10-year risk of death from causes other than breast cancer using CRR and NHS data because consideration of older women's 10-year life expectancy is as important as considering their breast cancer risk in making appropriate mammography screening decisions. We also aim to improve our model's generalizability by using Black Women's Health Study data and CRR to determine race-specific risk factor hazard ratios for breast cancer (BC) and non-BC death to use in our model and by calibrating our model to population based breast cancer and non-BC death incidence rates. We will examine our final model's performance in two diverse independent cohorts (WHI and the Multiethnic cohort) and will compare its performance in predicting breast cancer to that of the BCRAT and the Tyrer-Cuzick (TC) models since TC is also increasingly recommended for use in a general population. In sensitivity analyses, we will examine the effect of adding breast density and genomic data to our model. To make our model easily accessible we will add it to our widely used ePrognosis website and to ensure our model's webpage is user-friendly we will test its acceptability with end users. Reducing over- screening for breast cancer in older women is an NCI priority and we anticipate that use of our model will help optimize older women's use of mammography and as a result will improve their care and quality of life.

乳腺癌的发病率随着年龄的增长而增加。虽然乳房X光检查可以减少乳腺癌 40-74 岁女性的死亡率，>75 岁女性的疗效尚不清楚，平均需要 10.7 几年前，千分之一的女性因接受筛查而避免了乳腺癌死亡。也存在风险 进行筛查，其中包括过度诊断（检测非致命性肿瘤）。因此，指南建议 临床医生在决定筛查时会考虑老年女性患乳腺癌的风险和预期寿命。然而， 临床医生发现很难评估老年女性的乳腺癌风险和健康状况如何相互作用来确定 筛查的潜在好处是否大于风险。可能是因为现有模型不 专为老年女性开发，没有模型可以同时预测乳腺癌和非乳腺癌 癌症（BC）死亡有助于为这些决策提供信息。为了改善老年女性乳腺癌的预测，我们 之前开发了一种新模型来预测 55 岁以上绝经后女性的 5 年乳腺癌风险 多年使用竞争风险回归 (CRR) 和护士健康研究 (NHS) 的数据。我们然后 检查了我们的模型在女性健康倡议 (WHI) 参与者中的表现。我们的模型考虑 年龄、家族史、健康行为、生殖因素和健康状况来评估乳腺癌风险。我们 发现我们的模型准确预测了 55-74 岁女性的乳腺癌，但低估了 55-74 岁女性的乳腺癌 WHI 中 >75 岁的女性。我们的模型的歧视与乳腺癌风险评估工具的歧视相似 （BCRAT，初级保健中最常见的风险模型）在 WHI 中，但我们的模型准确地进行了风险分层 年长女性多于 BCRAT。在实施我们的模型之前，我们的目标是将其扩展到预测 10 年风险 使用 CRR 和 NHS 数据，由于考虑到老年人，导致除乳腺癌以外的原因导致的死亡 女性的 10 年预期寿命与在采取适当措施时考虑其乳腺癌风险同样重要 乳房X光检查筛查决定。我们还旨在通过使用 Black 来提高模型的通用性 女性健康研究数据和 CRR 以确定乳腺癌的种族特定危险因素风险比 (BC) 和非 BC 死亡用于我们的模型，并根据基于人群的乳腺癌校准我们的模型 和非 BC 死亡率。我们将在两个不同的独立环境中检查最终模型的性能 队列（WHI 和多种族队列）并将其在预测乳腺癌方面的表现与 BCRAT 和 Tyrer-Cuzick (TC) 模型，因为 TC 也越来越多地被推荐用于一般情况 人口。在敏感性分析中，我们将检查将乳腺密度和基因组数据添加到我们的研究中的效果 模型。为了使我们的模型易于访问，我们将其添加到我们广泛使用的 ePrognosis 网站并 确保我们模型的网页用户友好，我们将测试其最终用户的可接受性。减少过度 老年女性乳腺癌筛查是 NCI 的优先事项，我们预计使用我们的模型将有所帮助 优化老年妇女对乳房 X 光检查的使用，从而改善她们的护理和生活质量。