Understanding and Predicting Fatigue, CV Decline & Events After Breast CA Treatment

了解和预测疲劳、CV 下降

基本信息

批准号：
10481826
负责人：
William Gregory Hundley
金额：
$ 21.29万
依托单位：
WAKE FOREST UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10481826
关键词：
Acute Address Adjuvant Adjuvant Chemotherapy Adjuvant Therapy Age American Heart Association Anthracycline Behavioral Biological Markers Breast Breast Cancer Patient Breast Cancer Treatment Breast Cancer survivor Breast Cancer therapy Cancer Survivor Cardiac Death Cardiopulmonary Cardiotoxicity Cardiovascular Diseases Cardiovascular Physiology Cardiovascular system Categories Chronic Disease Clinical Cohort Studies Communities Community Clinical Oncology Program Community Hospitals Consumption Data Databases Development EFRAC Early Diagnosis Echocardiography Enrollment Epidemiology Equation Ethnic Origin Etiology Event Exercise Test FarGo Fatigue Fibrosis Functional disorder Funding Future Guidelines Healthcare Heart failure Image Impairment Incidence Inflammation Infrastructure Intervention Knowledge Left Left Ventricular Dysfunction Left Ventricular Ejection Fraction Left Ventricular Function Life Longitudinal Studies Magnetic Resonance Imaging Malignant Neoplasms Measures Mediating Mental Health Modeling Morbidity - disease rate Multicenter Studies Myocardial Myocardial Infarction Natural History Neoadjuvant Therapy Network-based Patient Self-Report Patients Physiologic pulse Population Preventive Quality of life Race Radiation Radiation therapy Research Research Design Research Personnel Risk Risk Factors SF-36 Savings Schedule Secure Serum Structure Surveillance Program Survival Rate Survivors Thick Time Toxic effect Ventricular Walking Woman base breast cancer diagnosis cancer therapy cardiovascular injury cardiovascular risk factor chemotherapy clinical center cohort cost design detection method exercise capacity exercise intolerance experience forest health related quality of life high risk hospital utilization improved malignant breast neoplasm mortality normal aging physical conditioning prediction algorithm predictive modeling prevent programs prospective psychosocial public health relevance risk prediction therapy design

项目摘要

DESCRIPTION (provided by applicant): Though the use of chemotherapy and early detection methods have greatly increased survival rates for women with Stage I-III breast cancer, this progress is threatened by a significant increase in cardiovascular (CV) events for survivors. Over 35% of women experience CV injury, left ventricular (LV) dysfunction, exercise intolerance, or fatigue after receipt of adjuvant chemotherapy (Adj-C) for Stage I-III breast cancer. In addition, CV events are the leading cause of morbidity and mortality for those surviving 5 to 8 years after their breast cancer diagnosis. Currently, little is understood about te origins of CV dysfunction, exercise intolerance, or fatigue in these women, and risk prediction algorithms for CV events do not incorporate breast cancer treatment in their models. We propose a study of baseline and serial longitudinal measures that will determine the influence of Adj-C with and without concomitant radiation treatment on 3 general categories: (1) CV function, (2) exercise capacity and fatigue, and (3) future development of CV events in a cohort of 1,000 women (840 with Stage I-III Breast Cancer and 160 healthy comparators). We also seek to answer questions about the relationship of alterations in LV and aortic function, what demographic risk factors and changes in serum biomarkers precede LV dysfunction, and if risk factor prediction models forecast development of CV events in the same cohort. Our longitudinal study design accounts for pre-existing and dynamic changes in risk factors during receipt of Adj-C and assesses subsequent CV events. This study has secured robust financial and infrastructure support through the Wake Forest NCI Community Oncology Research Program. This partnership reduces the overall cost of the current proposal by funding enrollment, staff reimbursement, regulatory oversight, study database development, echocardiograms, and an active surveillance program to ascertain the occurrence of CV events. This will be the first epidemiologic cohort study designed to fully characterize the time course and define the correlates of subclinical CV dysfunction, exercise intolerance, fatigue and CV events in women treated with Adj-C for breast cancer. A study of this magnitude is important to assess a variety of subjects so that the scientific community may better understand who is at risk and how we can prevent future CV damage. By enrolling patients in community hospitals and utilizing the unique expertise of each Co-Investigator, this study can capture the data needed to make such predictions. The immediate impact of this research will reduce the gaps in knowledge that prevent treatment design and generate risk prediction algorithms to avert CV events in breast cancer survivors. New knowledge from this study will quantify the risk of developing subclinical CV disease and exercise intolerance in this population, identify potential mechanisms, and inform future study development to reduce CV disease and improve overall survival in women with breast cancer.

描述（由适用提供）：尽管使用化学疗法和早期检测方法的使用对I-III期乳腺癌的女性的存活率大大提高，但这种进步受到了生存的心血管（CV）事件的显着增长的威胁。超过35％的女性遭受CV损伤，左心室（LV）功能障碍，运动摄入术或接受I-III期乳腺癌的调整化学疗法（ADJ-C）后疲劳。此外，在乳腺癌诊断后5到8年的生存中，简历事件是发病率和死亡率的主要原因。目前，关于这些女性的CV功能障碍，运动率或疲劳的TE起源，CV事件的风险预测算法并未在其模型中纳入乳腺癌治疗。我们提出了一项基线和串行纵向测量的研究，该研究将确定ADJ-C对有或不同时进行辐射处理对3个一般类别的影响：（1）CV功能，（2）运动能力和疲劳，以及（3）CV事件的未来在同类群体中的CV事件发展为1,000名女性（840名与I-I-III期乳房乳腺癌和160阶段的比较剂）。我们还试图回答有关LV和主动脉功能改变的关系的问题，血清生物标志物的哪些人口危险因素和LV功能障碍之前的变化以及如果风险因素预测模型预测同一队列中CV事件的开发。我们的纵向研究设计说明了在接收ADJ-C期间的风险因素和随后的CV事件评估期间的危险因素的现有和动态变化。这项研究通过Wake Forest NCI社区肿瘤研究计划获得了强大的财务和基础设施支持。这种伙伴关系通过资助入学率，员工报销，监管监督，研究数据库开发，超声心动图和积极的监视计划来降低当前提案的总体成本，以确定CV事件的发生。这将是第一项旨在充分表征时间过程并定义亚临床CV功能障碍，运动性摄入率，疲劳和CV事件的相关性的第一项流行病学队列研究。对这一规模的研究对于评估各种受试者很重要，以便科学界可以更好地了解谁处于危险之中以及我们如何防止将来的简历损害。通过将患者纳入社区医院并利用每个共同评估者的独特专业知识，本研究可以捕获做出预测所需的数据。这项研究的直接影响将减少知识的差距，以防止治疗设计并产生风险预测算法，以避免乳腺癌存活中的简历事件。这项研究的新知识将量化患有亚临床CV疾病并在该人群中进行运动的风险，确定潜在的机制，并为未来的研究开发提供信息，以减少CV疾病并改善乳腺癌女性的总体生存。