Long-term outcomes after coronary artery bypass grafting: Preoperative kidney function is prognostic

ObjectiveEnd-stage renal disease is an independent predictor of mortality after coronary artery bypass grafting. Limited information exists, however, regarding the impact of chronic kidney disease on long-term outcome after bypass grafting. The purpose of this study was to assess the impact of kidney function on long-term outcomes in patients undergoing coronary artery bypass grafting.MethodsWe studied 931 consecutive patients undergoing coronary artery bypass grafting in a single center. Demographic and clinical data were collected preoperatively. Chronic kidney disease was defined preoperatively according to the Modification of Diet in Renal Disease equation as an estimated glomerular filtration rate less than 60 mL · min−1 · 1.73 m−2. Multivariate Cox proportional hazard analyses were performed to determine the independent prognostic factors after bypass grafting. The primary outcome was a composite, combining death, acute coronary syndrome, stroke or transient ischemic attack, and coronary or peripheral revascularization during follow-up. Secondary outcomes were overall causes of death and cardiovascular death, acute coronary syndrome, and stroke or transient ischemic attack.ResultsOne hundred fourteen (12.2%) patients had preoperative chronic kidney disease (estimated glomerular filtration rate range, 20.5-59.8 mL · min−1 · 1.73 m−2). After a mean follow-up of 3.1 ± 1.4 years (median, 3.3 years), chronic kidney disease was found to be an independent predictor of the composite outcome (hazard ratio and 95% confidence interval, 1.46 [1.01-2.11]; P = .0467) and overall death (hazard ratio and 95% confidence interval, 1.89 [1.16-3.07]; P = .0106).ConclusionsBeyond the perioperative period, preoperative moderate-to-severe chronic kidney disease is an independent long-term predictor of cardiovascular events and total mortality after coronary artery bypass grafting. Chronic kidney disease status should be incorporated into prediction models and clinical risk assessments.