Cardiovascular Risk Prediction and Reduction in Men with Prostate Cancer

About This Grant

PROJECT SUMMARY Cardiovascular disease (CVD) is the leading cause of death in men with prostate cancer (PCa), the most common male cancer in the US. With substantial advancements in PCa care, men with non-metastatic PCa have high 5-year cancer survival rates exceeding 99%. However, in this older male population, non-PCa specific comorbidities are widely prevalent. CVD results in a 1.5-fold greater risk of mortality than PCa itself, accounting for 23% of all deaths. The increased risk of CVD in PCa is due to the high burden of cardiovascular risk factors, prevalent CVD, and exposure to androgen deprivation therapy (ADT). The consequences of these co-morbidities and toxicities in the aging male are that the oncologic benefits of ADT may be outweighed by the substantial CVD risk. To enhance the overall health of men with PCa, we need to effectively understand and manage CVD risk. However, current CVD risk stratification tools, including the widely used ACC/AHA Pooled Cohort Equation (PCE) and most current AHA PREVENT, perform poorly in cancer patients as they do not consider critical determinants of CV risk in cancer, such as ADT, the social determinants of health, or the competing risks of PCa- and non-CVD related mortality. Our long-term goal is to improve upon the overall health outcomes of men with PCa through risk-based CVD management, i.e. tailoring the intensity of CVD prevention and management to the individual patient risk. The overall objective of this R01 proposal is to develop accurate CVD absolute risk prediction models in men with PCa and determine their clinical impact. Our rationale is that current clinical CVD risk prediction tools, developed in non-cancer populations, are fundamentally limited, as they do not incorporate cancer-specific variables, cardiotoxic cancer treatment, or emerging predictors of growing importance, such as the social determinants of health (SDOH). They also do not account for competing risks. The following Specific Aims are proposed: Aim 1: Develop a well-phenotyped, population-based cohort study by augmenting, harmonizing, and validating EMR data from a large, diverse health system (Penn) with SEER-Medicare and medication data (Part D). Aim 2: Derive and externally validate PCa-specific, accurate CVD absolute risk prediction models that incorporate individual patient characteristics, SDOH, clinical and cancer treatment information. Aim 3: Evaluate the impact of model implementation on patient health outcomes using plasmode simulation studies. The expected outcome is a PCa-specific, practical CVD absolute risk prediction tool that can readily be implemented into clinical care. Moreover, our overall strategy in building a robust cohort study to enable accurate absolute risk modeling is highly generalizable to other cancers. This work will have an important positive impact, as it will develop a key risk assessment tool that will advance the overall health of men with PCa and establish a paradigm-shifting, highly rigorous scientific approach widely adaptable to all cancer populations.