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.

项目总结