Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations.
Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations.
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DOI：
10.1007/s11904-021-00567-w
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发表时间：
2021-08
影响因子：
4.6
通讯作者：
Triant VA
中科院分区：
文献类型：
--
作者：
Achhra AC;Lyass A;Borowsky L;Bogorodskaya M;Plutzky J;Massaro JM;D'Agostino RB Sr;Triant VA
提供HIV感染者（PLWH）心血管疾病（CVD）预测工具的发展和应用现状。针对普通人群开发的几种风险预测模型可用于预测心血管疾病风险，其中最值得注意的是美国的合并队列方程（PCE）、Framingham风险函数和欧洲的SCORE（系统性冠状动脉风险评估）。在PLWH队列的验证研究中，这些模型通常低估了心血管疾病的风险，特别是在年轻人、女性、黑人或预测处于低/中等风险的个体中。有一种hiv特异性CVD预测模型，即抗hiv药物不良事件数据收集（D:A:D）模型，但是它的表现一般，特别是在美国的队列中。用炎症或冠状动脉钙化的新型生物标志物增强CVD预测是有意义的，但尚未在PLWH中进行评估。最后，在全球范围内，不同类型的PLWH缺乏心血管疾病风险预测的研究。虽然可用于预测PLWH患者心血管疾病的风险模型仍不理想，但临床医生应对该人群中较高的心血管疾病风险保持警惕，并应使用任何这些风险评分进行风险分层，以指导预防性干预。关注已确立的传统风险因素，如吸烟，在PLWH中仍然至关重要。针对PLWH在不同环境下的风险预测功能将增强临床医生提供最佳预防护理的能力。
To provide the current state of the development and application of cardiovascular disease (CVD) prediction tools in people living with HIV (PLWH). Several risk prediction models developed on the general population are available to predict CVD risk, the most notable being the US-based pooled cohort equations (PCE), the Framingham risk functions, and the Europe-based SCORE (Systematic COronary Risk Evaluation). In validation studies in cohorts of PLWH, these models generally underestimate CVD risk, especially in individuals who are younger, women, Black race or predicted to be at low/intermediate risk. An HIV-specific CVD prediction model, the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, is available, but its performance is modest, especially in US-based cohorts. Enhancing CVD prediction with novel biomarkers of inflammation or coronary artery calcification is of interest but has not yet been evaluated in PLWH. Finally, studies on CVD risk prediction are lacking in diverse PLWH globally. While available risk models for CVD prediction in PLWH remain suboptimal, clinicians should remain vigilant of higher CVD risk in this population and should use any of these risk scores for risk-stratification to guide preventive interventions. Focus on established traditional risk factors such as smoking remains critical in PLWH. Risk prediction functions tailored to PLWH in diverse settings will enhance clinicians’ ability to deliver optimal preventive care.
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作者：
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