Modeling Best Approaches for Cardiovascular Disease Prevention in Cancer Survivors

模拟癌症幸存者心血管疾病预防的最佳方法

• 批准号: 10608446
• 负责人: Chung Yin Kong
• 金额: $ 71.3万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608446
• 关键词: Affect; Aspirin; Atherosclerosis; Attenuated; Biochemical; Breast; Calcium; Calibration; Cancer Model; Cancer Survivor; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cessation of life; Clinical Trials; Cost Effectiveness Analysis; Data; Decision Making; Development; Disease Outcome; General Population; Genetic; Genetic Risk; Goals; Guidelines; High Prevalence; Hyperlipidemia; Hypertension; Incidence; Life; Lipids; Lung; Malignant Neoplasms; Methods; Modeling; Morbidity - disease rate; Natural History; Outcome; Patients; Population; Population Heterogeneity; Populations at Risk; Prevention strategy; Preventive care; Primary Prevention; Prostate; Quality of life; Quality-Adjusted Life Years; Radiation therapy; Randomized, Controlled Trials; Recommendation; Recurrence; Recurrent Malignant Neoplasm; Regional Disease; Risk; Risk Factors; Risk Reduction; Source; Stratification; Survivors; Testing; Treatment-Related Cancer; United States; Woman; cancer recurrence; cancer risk; cancer therapy; cardiovascular disorder prevention; cardiovascular disorder risk; chemotherapy; chest computed tomography; cohort; comorbidity; comparative effectiveness; computed tomography screening; coronary calcium scoring; cost effectiveness; data harmonization; effectiveness evaluation; ethnic minority; genetic risk factor; improved; in silico; innovation; low dose computed tomography; men; models and simulation; mortality; multi-ethnic; novel; polygenic risk score; population based; racial minority; sex; simulation; targeted treatment

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.

项目摘要 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CVD。总的来说，有>6 百万BC，PC和LC幸存者在美国。虽然癌症是发病率和死亡率的主要来源， 大多数BC和PC以及许多早期LC幸存者死于合并症，特别是CVD。癌 幸存者的心血管疾病风险因素和心血管疾病本身的发病率都有所增加，而心血管疾病是心血管疾病的主要原因。 BC和PC幸存者的死亡率。在LC存活者中，CVD相关死亡占死亡率的约30%; 在越来越多的早期LC患者中，这一比例较高。在一般人群中， 用降脂剂进行初级预防（即，他汀类药物）和阿司匹林对降低CVD非常有效 发病率和死亡率，但这些指导方针的主要心血管疾病的预防不能外推到癌症 幸存者癌症幸存者的CVD预防方法需要考虑传统的风险因素 （包括遗传风险）以及某些癌症治疗的CVD风险。此外，竞争 癌症复发或合并症的风险可能会限制一级CVD预防的长期益处。 最后，癌症本身、癌症治疗相关的并发症和合并症的高患病率可以 对生活质量产生负面影响，并削弱质量调整预期寿命的绝对改善， 一级预防CVD的成本效益（CE）。缺乏适用于癌症幸存者的具体数据， 严重的负面影响，导致更糟糕的心血管结局。随机对照不太可能 评估CVD预防策略对癌症幸存者的益处的随机对照试验（RCT）将永远进行。 因此，迫切需要使用替代方法来优化预防保健建议， 人口增长。我们建议使用模拟建模，一种补充临床试验的方法， 评估不同癌症幸存者人群中CVD预防的危害，益处和CE。具体 目的是：（1）开发一个综合多种族癌症模型（IMEC），以纳入发展， BC、PC和LC幸存者的不同人群中CVD的进展和结局;（2）识别BC 将受益于CVD初级预防的幸存者，并确定其CE;（3）确定有效性 和CE的主要CVD预防PC幸存者;和（4）确定最有效的和CE 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，多样化和 具有全国代表性的、基于人群的癌症和心血管队列，以创建、校准和验证 IMEC（目标1）。然后，我们将使用该模型通过计算机模拟RCT来检验我们的假设（目标2-4）。我们 研究是创新的，使用最先进的建模方法和新的数据协调，统计和 模拟方法，以优化癌症幸存者CVD预防策略的使用。结果将 对大量幸存者的管理有直接影响，并指导患者决策。