Evaluation and development of cardiovascular risk prediction algorithms in HIV

HIV心血管风险预测算法的评估和开发

• 批准号: 8915903
• 负责人: Virginia Athena Triant
• 金额: $ 82.08万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-06 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8915903
• 关键词: Accounting; Adoption; Algorithms; American; American Heart Association; Anti-Retroviral Agents; Cardiology; Cardiovascular Diseases; Cardiovascular system; Caring; Categories; Cholesterol; Chronic; Clinical; Clinical Data; Control Groups; Coronary heart disease; Data; Data Sources; Development; Discrimination; Disease; Elements; Ensure; Epidemiology; Equation; Evaluation; Event; Functional disorder; Future; General Population; Goals; Grant; Guidelines; HIV; Health; Heart Diseases; Immune; Immunologics; Individual; Infection; Inflammation; Inflammatory; Knowledge; Low-Density Lipoproteins; Measurable; Methodology; Methods; Minority; Outcome; Patients; Performance; Personal Satisfaction; Pharmaceutical Preparations; Phase; Physicians; Population; Prevention; Public Health; Risk; Risk Assessment; Risk Factors; Risk Marker; Risk Reduction; Role; Smoking; Time; Woman; Work; abstracting; adjudicate; base; cardiovascular disorder prevention; cardiovascular disorder risk; cardiovascular risk factor; clinical practice; cohort; college; design; experience; high risk; immune activation; improved; interdisciplinary approach; novel; tool

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.

描述（由申请人提供）： 项目摘要/摘要 心血管疾病 (CVD) 是 HIV 感染者面临的一个重大问题，但最新的 CVD 风险预测工具在多大程度上准确预测 HIV 患者的风险尚不清楚。在这笔赠款中，我们将 评估美国心脏病学会 (ACC)/美国心脏协会 (AHA) 2013 年 11 月发布的新 CVD 风险预测算法，以评估其在 HIV 患者中的表现。然后，我们将开发一种新的风险预测算法，结合艾滋病毒和艾滋病毒相关因素来尝试改进风险预测。进行这项研究的基本原理是 HIV 相关 CVD 的独特病理生理学，人们认为传统危险因素不能完全解释这一现象，并且在很大程度上是由炎症和免疫失调驱动的。虽然现有的 CVD 风险预测工具已应用于 HIV 群体，但没有证据表明它们适合使用，因为它们不能反映这些潜在的免疫和炎症变化。准确预测 CVD 风险尤为重要，因为它是新发布的 2013 年胆固醇指南的关键组成部分，该指南指导临床医生识别需要 CVD 风险调整治疗的患者。通过拟议研究的目标 1，我们将评估新的 ACC/AHA CVD 风险预测算法，评估其准确预测 HIV 患者 CVD 风险的程度，并假设其预测风险不足。我们将对长期存在的弗雷明汉风险评分进行并行分析。在目标 2 中，我们将开发一种专为 HIV 人群使用的新 CVD 风险预测算法，首次将 HIV 状态作为 CVD 风险因素纳入预测函数中，并假设将其纳入将改善风险预测，超越传统 CVD仅危险因素。我们将通过评估纳入表明疾病和治疗状况的艾滋病毒相关变量是否会比单独纳入艾滋病毒状况进一步改善风险预测来完善这一分析。为了实现这些目标，我们将利用一个专门适合进行研究的既定队列，该研究由具有相关领域特定专业知识的跨学科团队进行。我们将与在风险预测统计分析方面拥有数十年经验的 Ralph D'Agostino 博士及其团队合作，应用复杂的风险预测方法，以便严格比较现有的和新的风险预测功能。心血管风险预测是​​艾滋病毒相关心脏的一个重要方面 疾病，因为它是临床医生识别需要风险调整治疗的高风险个体的能力的基础。在不同环境中验证新的 ACC/AHA 风险预测算法并评估新型风险标记的益处是新的 2013 ACC/AHA 风险评估指南中确定的具体优先事项，这两项工作都将通过本研究进行。拟议的研究提供了一个机会来及时回答具有重大临床和公共卫生影响的问题，优化艾滋病毒 CVD 风险预测方法，从而改进该高危人群的 CVD 预防策略。