Cardiovascular risk prediction in people with chronic kidney disease

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)518-523
Number of pages6
JournalCurrent opinion in nephrology and hypertension
Volume25
Issue number6
DOIs
StatePublished - Nov 1 2016

Keywords

  • albuminuria
  • cardiovascular disease
  • estimated glomerular filtration rate
  • risk prediction

ASJC Scopus subject areas

  • Internal Medicine
  • Nephrology

Fingerprint

Dive into the research topics of 'Cardiovascular risk prediction in people with chronic kidney disease'. Together they form a unique fingerprint.

Cite this