Baseline Symptoms and Adherence to Oral Breast Cancer Therapy among Older Women

对于每年发生激素受体阳性乳腺癌的> 200，000名老年妇女， 内分泌治疗（AET）至少5年可提高总生存率。然而，几乎一半的患者 AET未能服用所有药丸。在这一背景下和其他一些背景下进行的教育和基于教师的研究 对依从性的影响小得令人不安，更复杂的干预措施已被证明难以 反对有效。在这项建议中，我们寻求更好地了解妇女描述为主要的症状， AET依从性的障碍。我们利用先前的研究表明，预先存在的症状可能会发挥作用， 在患者AET经历中的重要作用。我们的建议填补了我们理解中的关键空白， 检查在常规护理实践中接受治疗的年龄较大和选择较少的受试者，并通过使用 已经在美国各地定期收集。具体而言，我们将利用国家综合 癌症网络的痛苦温度计（DT）及其伴随的问题清单，以前已经被 显示出老年乳腺癌妇女的症状负担很高，但还没有 检查与AET相关或同时考虑老年人的广泛共病状况， 妇女大多数癌症中心在诊断或早期护理时管理DT和问题列表， 为早期干预提供信息，以帮助妇女继续进行AET。我们将调查DT是否 和问题列表是老年妇女AET中止的预测因素，具体目标如下： 1检查患者报告的诊断时的痛苦与早期停止治疗的相关性。 AET。我们将利用一组绝经后激素受体相关事件的电子健康记录数据， 阳性乳腺癌患者，以检查诊断时测量的DT（评分为0 - 10） 在至事件发生时间分析中AET停药，考虑年龄、骨关节炎和其他合并症， 和其他乳腺癌治疗方法。目标2.探讨患者报告的症状与 诊断为AET中止。在同一队列中，使用相同的一般方法，我们将 探索AET停药和a）基于DT问题列表的患者报告的总体症状计数）和B） 症状群（例如，疲劳、睡眠、集中）。我们将使用标准回归和创新的 使用贝叶斯加性回归树的机器学习。通过这种方法，我们将生产 通过提供一种识别高风险的方法， 患者是开发新干预措施的主要目标。它也可能是一个重要的 在老年患者中采用积极治疗方案治疗的其他疾病的模型， 对于很多老年患者来说。

For the >200,000 older women who develop hormone receptor positive breast cancer yearly, adjuvant endocrine therapy (AET) for at least five years increases overall survival. Yet almost half of patients prescribed AET fail to take all their pills. Education- and reminder-based studies in this and a number of other settings have had disappointingly small effects on adherence, and more complex interventions have proven difficult to sustain. In this proposal we seek a better understanding of the symptoms that women describe as major barriers to AET adherence. We draw on prior research suggesting that preexisting symptoms may play an important role in patients’ experience with AET. Our proposal fills crucial gaps in our understanding by examining older and less selected subjects treated in a usual care practice, and by using a measure that is already routinely collected across the United States. Specifically, we will use the National Comprehensive Cancer Network’s Distress Thermometer (DT) and its accompanying problem list, which have previously been shown to reveal a high burden of symptoms among older women with breast cancer but have not been examined in association with AET or while accounting for the wide spectrum of comorbid conditions of older women. Most cancer centers administer the DT and problem list at diagnosis or earlier in care, increasing their value for informing early intervention to help women continue their AET. We will investigate whether the DT and problem list are predictive of AET discontinuation among older women, with specific aims as follows: Aim 1 To examine the association of patient-reported distress at diagnosis with early discontinuation of AET. We will utilize electronic health record data from a cohort of incident postmenopausal hormone receptor positive breast cancer patients to examine the association of the DT (scored 0 to 10) measured at diagnosis with AET discontinuation in time-to-event analyses, accounting for age, osteoarthritis, and other comorbidities, and other breast cancer treatments. Aim 2. To explore the association of patient-reported symptoms at diagnosis with discontinuation of AET. In the same cohort and using the same general approach, we will explore AET discontinuation and a) patient-reported overall symptom count based on DT problem list) and b) symptom clusters (e.g., fatigue, sleep, concentration). We will use both standard regression and an innovative application of machine learning using Bayesian Additive Regression Trees. With this approach, we will produce immediately actionable findings to improve adherence outcomes, by providing a means of identifying high-risk patients who are the primary targets for the development of new interventions. It could also be an important model for other conditions that are treated with aggressive treatment regimens in older patients, informing care for a large number of older patients.