Evaluation and development of cardiovascular risk prediction algorithms in HIV

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DESCRIPTION (provided by applicant): Project Summary/Abstract Cardiovascular disease (CVD) is a significant problem for HIV-infected patients, yet the extent to which the newest CVD risk prediction tools accurately predict risk for HIV patients is not known. In this grant, we will evaluate the new American College of Cardiology (ACC)/ American Heart Association (AHA) CVD risk prediction algorithm, released in November 2013, to assess its performance in HIV patients. We will then develop a new risk prediction algorithm incorporating HIV and HIV-related factors to attempt to improve risk prediction. The rationale for performing this study is the uniqu pathophysiology underlying HIV-associated CVD, which is thought to be incompletely explained by traditional risk factors and driven in large part by inflammation and immune dysregulation. While established CVD risk prediction tools have been applied to HIV groups, there is not evidence that they are appropriate for use as they do not reflect these underlying immunologic and inflammatory changes. Accurate prediction of CVD risk is particularly important as it is a key component of the newly released 2013 cholesterol guidelines, which guide clinicians in identifying patients in need of CVD risk modifying treatment. Through Aim 1 of the proposed study, we will evaluate the new ACC/AHA CVD risk prediction algorithm, assessing the degree to which it accurately predicts CVD risk for HIV patients and hypothesizing that it will under predict risk. We will conduct a parallel analysis of the longstanding Framingham Risk Score. In Aim 2, we will develop a new CVD risk prediction algorithm tailored for use in HIV populations, for the first time incorporating HIV status as a CVD risk factor within a prediction function and hypothesizing that its inclusion will improve risk prediction beyond that by traditional CVD risk factors alone. We will refine this analysis by assessing whether inclusion of HIV-related variables indicating disease and treatment status further improve risk prediction beyond inclusion of HIV status alone. To achieve these aims, we will leverage an established cohort uniquely suited to perform the study to be conducted by a cross- disciplinary team assembled with specific expertise in the relevant fields. We will collaborate with Dr. Ralph D'Agostino and his team, who have decades of experience in risk prediction statistical analysis, to apply sophisticated risk prediction methodology in order to rigorously compare established and new risk prediction functions. Cardiovascular risk prediction is a critical aspect of HIV-related heart disease as it underlies a clinician's ability to identify high-risk individuals in need of risk-modifying therapy. Validating the new ACC/AHA risk prediction algorithm in diverse settings and assessing the benefit of novel risk markers were specific priorities identified in the new 2013 ACC/AHA risk assessment guidelines, both of which will be performed through this study. The proposed study presents an opportunity to answer a timely question with a significant clinical and public health impact, optimizing methods for CVD risk prediction in HIV and thereby improving CVD preventative strategies for this at-risk group.