Cardiovascular risk prediction in people with chronic kidney disease

Abstract

Purpose of review: Clinical guidelines are not consistent regarding whether or how to utilize information on measures of chronic kidney disease (CKD) for predicting the risk of cardiovascular disease (CVD). This review summarizes recent literature regarding CVD prediction in the context of CKD.

Recent findings: Previous studies used different definitions of CKD measures and CVD outcomes, and applied distinct statistical approaches. A recent individual-level meta-analysis from the CKD Prognosis Consortium is of value as it has uniformly investigated creatinine-based estimated glomerular filtration rate (eGFR) and albuminuria as CKD measures and applied the same statistical approach across 24 cohorts with more than 630 000 participants. In this meta-analysis, eGFR and albuminuria improve CVD risk prediction beyond traditional CVD risk factors, particularly for CVD mortality and heart failure. Albuminuria demonstrates more evident improvement than eGFR. Moreover, several recent studies have shown that other filtration markers, for example, cystatin C and β2-microglobulin, and measures of atherosclerosis or cardiac damage (e.g., coronary artery calcium and cardiac troponins) can further improve CVD prediction in the CKD population.

Summary: Future clinical guidelines may require updates regarding whether/how to incorporate CKD measures and other biomarkers in CVD prediction, depending on the CVD outcomes of interest, target population, and availability of those measures/biomarkers in that population.

Figure 1

Difference in C-statistic for cardiovascular outcomes by adding kidney measure(s) to traditional models in the combined general population and high-risk cohorts.

Figure 2

C-statistic difference for four cardiovascular outcomes by omitting kidney disease measures and traditional risk factors from a model with all risk factors in a CKD population