Modeling Best Approaches for Cardiovascular Disease Prevention in Cancer Survivors

项目总结 该提案的总体目标是确定心血管疾病一级预防的最佳策略 乳腺癌(BC)、前列腺癌(PC)和肺癌(LC)幸存者的疾病(CVD)。总而言之，&gt；6 百万公元前、PC和LC的幸存者在美国。虽然癌症是发病率和死亡率的主要来源，但 大多数BC和PC以及许多早期LC幸存者死于并存疾病，特别是CVD。癌 幸存者的心血管危险因素和心血管疾病本身的发生率都增加了，而心血管疾病是导致 BC和PC幸存者的死亡。在LC幸存者中，与心血管疾病相关的死亡约占死亡率的30%； 在越来越多的早期LC患者中，这一比例更高。在普通人群中， 降脂药物(即他汀类药物)和阿司匹林的一级预防对减少心血管疾病非常有效 发病率和死亡率，但这些心血管疾病初级预防指南不能推断为癌症 幸存者。癌症幸存者预防心血管疾病的方法需要考虑传统的危险因素 心血管疾病的风险(包括遗传风险)以及某些癌症治疗带来的心血管风险。此外，竞争 癌症复发或合并症的风险可能会限制初级心血管疾病预防的长期益处。 最后，癌症本身，癌症治疗相关的并发症，以及较高的并存率可以 对生活质量产生负面影响，削弱经质量调整的预期寿命和 心血管疾病一级预防的成本效益(CE)。缺乏适用于癌症幸存者的具体数据 深刻的负面影响，导致更糟糕的心血管结果。随机控制的可能性不大 评估心血管疾病预防策略对癌症幸存者的益处的试验(RCT)将永远进行。 因此，迫切需要使用替代方法来优化为此提出的预防保健建议。 不断增长的人口。我们建议使用模拟建模，这是一种补充临床试验的方法 评估心血管疾病预防在不同癌症幸存者群体中的危害、益处和CE。具体的 目的是：(1)开发一个综合的多种族癌症模型(IMEC)，以纳入该发展， BC、PC和LC幸存者中不同人群的心血管疾病进展和结果；(2)确定BC 幸存者将从初级心血管疾病预防中受益并确定CE；(3)确定有效性 和CE；以及(4)确定最有效的和CE的CVD LC幸存者的预防策略。为了实现这些目标，我们将使用来自几个大型、不同和 创建、校准和验证具有全国代表性的基于人群的癌症和心血管疾病队列 IMEC(目标1)。然后，我们将使用该模型通过进行电子随机对照试验(目标2-4)来检验我们的假设。我们的 研究在使用最先进的建模方法和新颖的数据协调、统计和 在癌症幸存者中优化使用心血管疾病预防策略的模拟方法。结果将会是 对大量幸存者的管理有直接影响，并指导患者决策。

PROJECT SUMMARY The overall goal of this proposal is to identify optimal strategies for primary prevention of cardiovascular disease (CVD) for survivors of breast (BC), prostate (PC) and lung (LC) cancer. All together, there are >6 million BC, PC and LC survivors in the US. While cancer is a major source of morbidity and mortality, the majority of BC and PC as well as many early-stage LC survivors die of comorbidities, particularly CVD. Cancer survivors have increased rates of both CVD risk factors as well as CVD itself, and CVD is the leading cause of death among BC and PC survivors. Among LC survivors, CVD-related deaths account for ~30% of mortality; this proportion is higher among the growing number identified with early-stage LC. In the general population, primary prevention with lipid-lowering agents (i.e., statins) and aspirin is highly effective for decreasing CVD incidence and mortality, but these guidelines for primary CVD prevention cannot be extrapolated to cancer survivors. Approaches for CVD prevention in cancer survivors need to consider traditional risk factors (including genetic risks) for CVD as well as CVD risk from certain cancer treatments. Additionally, competing risks from cancer recurrence or comorbidities may limit the long-term benefits of primary CVD prevention. Finally, the cancer itself, cancer treatment-related complications, and a higher prevalence of comorbidities can negatively impact quality-of-life and attenuate the absolute improvement in quality-adjusted life expectancy and the cost effectiveness (CE) of primary CVD prevention. Lack of specific data applicable to cancer survivors has profound negative impact, resulting in worse cardiovascular outcomes. It is unlikely that randomized controlled trials (RCT) assessing the benefits of CVD preventive strategies for cancer survivors will be ever conducted. Thus, there is an urgent need to use alternative methods to optimize preventive care recommendations for this growing population. We propose using simulation modeling, an approach complementary to clinical trials, to assess the harms, benefits, and CE of CVD prevention in diverse populations of cancer survivors. The Specific Aims are to: (1) Develop an Integrated Multi-Ethnic Cancer model (IMEC) to incorporate the development, progression, and outcomes of CVD among a diverse population of BC, PC and LC survivors; (2) Identify BC survivors who will benefit from and determine the CE of primary CVD prevention; (3) Determine effectiveness and CE of primary CVD prevention in PC survivors; and (4) Determine the most effective and CE CVD prevention strategies for LC survivors. To achieve these Aims, we will use data from several large, diverse and nationally representative, population-based cancer and cardiovascular cohorts to create, calibrate, and validate IMEC (Aim 1). Then, we will use the model to test our hypothesis by conducting in-silico RCTs (Aims 2-4). Our study is innovative in using state-of-the-art modeling methods and novel data harmonization, statistical and simulation approaches to optimize the use of CVD preventive strategies in cancer survivors. The results will have direct implications for the management of large numbers of survivors and guide patient decision-making.