Risk prediction of breast cancer treatment-related cardiotoxicity to guide clinical decision making

项目摘要 美国300万乳腺癌幸存者中有近20%患有心血管疾病（CVD）。的 美国国家癌症研究所、NHLBI、专业肿瘤学和心脏病学协会都支持 通过早期识别和干预降低乳腺癌幸存者CVD负担的重要性。 尽管诊断为I至III期乳腺癌的女性具有良好的预后， 存活率> 90%，已报道特异性辅助疗法导致损害患者的心血管（CV）事件。 健康相关生活质量和/或导致过早CVD死亡。CV事件，包括急性心肌梗死 据报道，梗死、中风和静脉血栓栓塞与辅助治疗有关。 化疗、生物制剂、放射疗法和/或激素疗法。这些治疗相关CV 这些事件构成了一个重大的公共卫生问题，因为它们将影响越来越多的乳腺癌患者。 幸存者的健康相关的生活质量在较长的预期寿命。目前，没有标准的风险模型， 预测在存在以下情况下与多种辅助乳腺癌治疗相关的CV事件风险： 已确定的CV风险因素（如高血压、高脂血症、吸烟），以告知实践 指导方针，促进共同的临床决策。这样的模型可以在治疗前告知女性 关于替代治疗策略的CVD潜在风险，同时保持最佳的机会， 癌症治疗这些模型还可以帮助确定治疗后CVD风险最高的女性， 通过常规成像和/或使用预防性药物，可能从更早和更密集的CV监测中获益 降低CV事件风险的药物。为了解决这一差距，我们的研究将创建风险预测模型， 分析了一个大型的，人口统计学异质性的成年女性队列（N= 40，500）， I期至III期浸润性乳腺癌在现实世界的医疗保健环境。我们将研究被诊断为 2008-2020年，并使用全球最大的健康中心之一的综合电子记录进行了长达15年的随访。 在美国的计划，凯撒永久。在目标1中，我们将评估新发CV事件（急性心肌梗死， 卒中、心力衰竭），调整肿瘤特征和CVD风险 年龄、种族/民族、既存CVD、CVD药物（他汀类药物、抗高血压药物、抗 糖尿病）、高血压、糖尿病、BMI和吸烟。然后，我们将估计CV事件的风险是否 在乳腺癌队列中比在年龄、种族匹配的无癌队列中更高。在目标2中，我们将创建 并验证早期（<1年）和晚期（长达15年）CV事件的风险预测模型。我们的项目将是 第一个估计多种已确定的CVD风险因素与乳腺癌辅助风险的关联 真实世界、种族和社会经济多样化的社区队列中的治疗相关CV事件。 我们的风险预测模型将提供新的信息来指导循证临床决策 关于乳腺癌的辅助治疗用途以及同时和治疗后的心脏肿瘤护理。

PROJECT SUMMARY Nearly 20% of the 3 million breast cancer survivors in the U.S. have cardiovascular disease (CVD). The National Cancer Institute, NHLBI, and professional oncology and cardiology societies have all endorsed the importance of reducing CVD burden in breast cancer survivors through earlier recognition and intervention. Although women diagnosed with stages I to III breast cancer have an excellent prognosis with 5-year relative survival >90%, specific adjuvant therapies have been reported to lead to cardiovascular (CV) events that impair health-related quality of life and/or lead to premature CVD death. CV events including acute myocardial infarction, stroke, and venous thromboembolism have been reported to be associated with adjuvant chemotherapy, biological agents, radiation therapy, and/or hormonal therapies. These treatment-related CV events pose a significant public health problem because they will affect the increasing number of breast cancer survivors’ health-related quality of life over a long-life expectancy. Currently, no standard risk model exists to predict the risk of CV events associated with multiple adjuvant breast cancer therapies in the presence of established CV risk factors (such as hypertension, hyperlipidemia, smoking) to inform practice guidelines and promote shared clinical decision-making. Such models can inform women before treatment about the potential risks of CVD from alternative treatment strategies while maintaining the best chances for cancer cure. These models can also help to identify women at highest risk of CVD after therapy who would potentially benefit from earlier and more intensive CV monitoring via routine imaging and/or use of preventive medications to mitigate risk of CV events. To address this gap, our study will create risk prediction models by analyzing a large, demographically heterogeneous cohort of adult women (N=40,500) with newly diagnosed stages I to III invasive breast cancer in real-world health care settings. We will study women diagnosed from 2008-2020 and followed up to 15 years using the comprehensive electronic records of one of the largest health plans in the U.S., Kaiser Permanente. In Aim 1, we will assess incident CV events (acute myocardial infarction, stroke, heart failure) following adjuvant breast cancer therapies, adjusting for tumor characteristics and CVD risk factors such as age, race/ethnicity, pre-existing CVD, CVD medications (statins, anti-hypertensives, anti- diabetics), hypertension, diabetes, BMI, and smoking. We will then estimate whether the risk of CV events is greater in the breast cancer cohort versus an age, race- matched cancer-free cohort. In Aim 2, we will create and validate risk prediction models for early (<1 year) and late (up to 15 years) CV events. Our project will be the first to estimate the association of multiple established CVD risk factors with the risk of breast cancer adjuvant treatment-related CV events in a real-world, ethnically and socioeconomically diverse community-based cohort. Our risk prediction models will provide new information to guide evidence-based clinical decision-making concerning adjuvant therapy use for breast cancer and concurrent and post-treatment cardio-oncology care.