In:
Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 129, No. suppl_1 ( 2014-03-25)
Abstract:
Introduction: Despite the high risk of cardiovascular disease (CVD) in those with chronic kidney disease (CKD), there are conflicting data as to whether two key kidney measures, estimated glomerular filtration rate (eGFR) and albuminuria, contribute to better CVD prediction, beyond conventional risk factors, warranting a more comprehensive investigation over a broad range of populations. Methods: We studied 127,825 participants without history of CVD from 12 general population, 3 high risk and 1 CKD cohorts with data on eGFR (based on the CKD-EPI creatinine equation) and urinary albumin-creatinine ratio (ACR) and at least 4 years of median follow-up for CVD mortality (4,133 deaths from 15 cohorts), coronary heart disease (CHD) (5,420 events from 9 cohorts), stroke (2,651 events from 9 cohorts), or heart failure (2,507 events from 8 cohorts). To compare eGFR and ACR with conventional predictors independently of the order of modeling, we examined the worsening of 5-year prediction of CVD outcomes by omitting each predictor in turn compared to a full model with all kidney and conventional predictors. Results: C-statistics for full models ranged from 0.759-0.836 in general population and high risk cohorts and 0.712-0.796 in the CKD population (Table). All the conventional and kidney measures contributed to better prediction of CVD outcomes. The contribution of ACR was greater than that of any conventional modifiable risk factors except in predicting CHD in both general/high-risk cohorts and CKD population. Although weaker than ACR, eGFR also contributed significantly to better prediction of CVD mortality (especially in CKD populations) and CHD. Largely similar results were observed for categorical net reclassification index. Conclusion: The two key kidney measures (particularly albuminuria) contribute as much as some or all of the conventional risk factors to CVD prediction, supporting their use for CVD risk classification in certain circumstances.
Type of Medium:
Online Resource
ISSN:
0009-7322
,
1524-4539
DOI:
10.1161/circ.129.suppl_1.55
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2014
detail.hit.zdb_id:
1466401-X
