Impact of Renal Disease Markers on Cardiovascular Disease Risk Prediction When Coronary Calcium Score Is Known

已知冠状动脉钙化评分时,肾脏疾病标志物对心血管疾病风险预测的影响

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Abstract

BACKGROUND: Markers of renal function have been added to the Predicting Risk of Cardiovascular Disease Events and Systematic Coronary Risk Evaluation cardiovascular risk calculators to enhance risk prediction. Here we examine the role of estimated glomerular filtration (eGFR) and urine albumin creatinine ratio (UACR)-and related dichotomous cut points (eGFR <60 mL/minute per 1.73 m(2) and albuminuria ≥30 mg albumin/gram creatinine)-for prediction of coronary heart disease, cardiovascular disease (CVD) outcomes (CVD mortality, heart failure, stroke, total CVD), and total mortality, using the MESA (Multi-Ethnic Study of Athersclerosis) calculator, which includes coronary artery calcium scores as a predictor in addition to traditional CVD risk factors. METHODS: The study included 6707 participants without clinical CVD with coronary artery calcium scoring. Cox proportional hazards models, adjusted for covariates including coronary artery calcium, were used to gauge the association of UACR, eGFR, albuminuria, and eGFR<60 with outcomes and change in disease prediction by area under the curve compared with models not including renal variables. Prespecified subgroups-age, sex, race or ethnicity, diabetes-were examined. RESULTS: UACR and albuminuria were significantly associated with most study outcomes; eGFR and eGFR<60 were less consistently related. Albuminuria significantly increased disease prediction for heart failure and for total mortality (P<0.001) but not for other outcomes. When prespecified subgroups were examined, UACR and albuminuria significantly improved prediction for many outcomes in participants >65 years of age, and for all outcomes in participants with diabetes. CONCLUSIONS: When coronary artery calcium is known, albuminuria improves heart failure prediction. Albuminuria and UACR each improve total mortality prediction as well. UACR and albuminuria improve prediction for all outcomes in people with diabetes.

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