Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

医学 背景(考古学) 肾脏疾病 风险评估 疾病 流行病学 弗雷明翰风险评分 重症监护医学 内科学 古生物学 计算机安全 计算机科学 生物
作者
Sadiya S. Khan,Josef Coresh,Michael J. Pencina,Chiadi E Ndumele,Janani Rangaswami,Sheryl L. Chow,Latha Palaniappan,Laurence S. Sperling,Salim S. Virani,Jennifer E. Ho,Ian J. Neeland,Katherine R. Tuttle,Radhika Rajgopal Singh,Mitchell S.V. Elkind,Donald M. Lloyd‐Jones
出处
期刊:Circulation [Ovid Technologies (Wolters Kluwer)]
卷期号:148 (24): 1982-2004 被引量:19
标识
DOI:10.1161/cir.0000000000001191
摘要

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.
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