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

老年女性的基线症状和口腔乳腺癌治疗的依从性

基本信息

批准号：
10261485
负责人：
Kathryn E Flynn
金额：
$ 19.5万
依托单位：
MEDICAL COLLEGE OF WISCONSIN
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10261485
关键词：
Accounting Address Adherence Age Aromatase Inhibitors Arthralgia Attention Breast Cancer Patient Breast Cancer Treatment Breast Cancer therapy Cancer Center Caring Chronic Clinical Trials Complex Data Degenerative polyarthritis Development Diagnosis Distress Early Intervention Education Electronic Health Record Estrogens Event Fatigue Informatics Intervention Interview Machine Learning Measures Modeling Morbidity - disease rate National Comprehensive Cancer Network Observational Study Oral Outcome Outcome Measure Pain Patient Outcomes Assessments Patients Pharmaceutical Preparations Play Population Postmenopause Randomized Controlled Clinical Trials Recurrence Relative Risks Reporting Research Research Design Role Sampling Sleep Statistical Models Symptoms Tamoxifen Therapy Clinical Trials Thermometers Time Treatment Effectiveness Treatment Protocols United States Vasomotor Woman adjuvant endocrine therapy aggressive therapy base clinical care clinical practice cohort comorbidity cost effective electronic data experience high risk hormone receptor-positive improved innovation malignant breast neoplasm mortality multidisciplinary older patient older women pill poor sleep prevent randomized trial regression trees secondary analysis side effect symptom cluster theories therapy adherence treatment as usual tumor

项目摘要

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.

对于每年超过 200,000 名患激素受体阳性乳腺癌的老年女性，辅助治疗内分泌治疗 (AET) 至少五年可提高总体生存率，但几乎一半的患者接受了治疗。 AET 在这种情况和许多其他情况下未能服用所有基于教育和提醒的研究。令人失望的是，对依从性的影响很小，而且事实证明更复杂的干预措施很难在本提案中，我们寻求更好地了解女性所描述的主要症状。我们借鉴先前的研究表明，先前存在的症状可能会产生影响。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老年和较少选择的受试者，并通过使用以下措施具体来说，我们将使用国家综合数据。 Cancer Network 的遇险温度计 (DT) 及其随附的问题列表，此前已研究表明，患有乳腺癌的老年女性的症状负担很重，但尚未得到证实与 AET 相关的检查或同时考虑老年人的广泛合并症大多数癌症中心在诊断时或护理早期都会进行 DT 和问题清单，从而增加她们的机会。为帮助女性继续进行 AET 提供早期干预的价值我们将调查 DT 是否有效。和问题清单可预测老年女性 AET 的终止，具体目标如下： 1 检查患者在诊断时报告的痛苦与早期停止治疗的关系 AET。我们将利用来自绝经后激素受体队列的电子健康记录数据。阳性乳腺癌患者检查诊断时测量的 DT（得分 0 至 10）之间的关联考虑到年龄、骨关节炎和其他合并症，在事件发生时间分析中停止 AET，目标 2. 探讨患者报告的症状之间的关联。诊断并停止 AET 在同一队列中并使用相同的一般方法，我们将探讨 AET 停用和 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、注意力）我们将使用标准回归和创新回归。使用贝叶斯加性回归树应用机器学习，我们将产生。通过提供识别高风险的方法，立即采取行动的结果来提高依从性结果作为开发新干预措施的主要目标的患者也可能是一个重要的目标。老年患者接受积极治疗方案治疗的其他疾病的模型，为护理提供信息对于大量老年患者来说。