Advances in HIV treatment have dramatically changed the course of HIV disease. The focus of clinical care has shifted from treating opportunistic infections to managing chronic, non-AIDS-related complications. Cardiovascular disease (CVD) has emerged as a major challenge to the long-term health of HIV patients, who face a higher risk than patients without HIV disease because of specific, HIV-related factors which accelerate the development of CVD. The management of CVD and other co-morbidities may differ in the setting of HIV, and significant questions regarding CVD risk prediction and prevention remain unanswered. To provide the best care to HIV populations as they age, there is an urgent need to develop rigorous, evidence-based, HIV- specific CVD risk prediction strategies. Through the proposed study, we will evaluate and develop methods to accurately predict CVD risk in HIV patients, addressing a major knowledge gap in HIV clinical care. These analyses are particularly timely given the paradigm shift occurring in the field of CVD prevention, with the recent development and introduction of a new American College of Cardiology (ACC)/American Heart Association (AHA) risk prediction algorithm which is a key component of newly released cholesterol guidelines. We propose several novel analyses, including 1) the first evaluation of the new ACC/AHA CVD risk prediction algorithm in the setting of HIV; and 2) the first incorporation of HIV itself into CVD risk prediction models. Our study will address the following aims:
Specific Aim 1 : To assess the performance of existing cardiovascular risk prediction algorithms in an HIV clinical care cohort Specific Aim 2: To generate and internally and externally validate new cardiovascular risk prediction algorithms for tailored use in HIV populations To achieve these aims, we will leverage a multidisciplinary team that is comprised of internationally-recognized leaders with the required expertise in relevant fields. Dr. Ralph D?Agostino will serve as Co-Principal Investigator and will apply his decades of experience in risk prediction statistical analysis to lead a team to validate and develop the new risk prediction algorithms. The HIV cohorts which will be used to generate and externally validate the newly-developed HIV-specific risk prediction algorithms are uniquely suited for these analyses and have produced seminal studies in the field of HIV and CVD. Our proposed work aligns with specific research priorities identified in the new 2013 ACC/AHA risk assessment guidelines which include validation of the new CVD risk prediction algorithm in diverse clinical settings. Our findings will have a significant clinical and public health impact, optimizing CVD risk prediction in HIV and informing CVD prevention strategies to improve the long-term health for this population.
Cardiovascular disease (CVD) poses a major long-term health risk for an aging HIV population, yet it is not known how to accurately predict CVD risk HIV patients. This study will assess whether existing CVD risk prediction tools are accurate for HIV patients. Through the study, we will: 1) develop a CVD risk prediction tool tailored to HIV, for the first time directly incorporating HIV as a CVD risk factor into prediction models; and 2) inform public health strategies by providing an evidence-based, clinically applicable tool to identify HIV patients at high CVD risk.
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