Background: Renal insufficiency after coronary artery bypass graft (CABG) surgery is associated with increased short-term and long-term mortality. We hypothesized that preoperative patient characteristics could be used to predict the patient-specific risk of developing postoperative renal insufficiency.
Methods and results: Data were prospectively collected on 11,301 patients in northern New England who underwent isolated CABG surgery between 2001 and 2005. Based on National Kidney Foundation definitions, moderate renal insufficiency was defined as a GFR <60 mL/min/1.73 m2 and severe renal insufficiency as a GFR <30. Patients with at least moderate renal insufficiency at baseline were eliminated from the analysis, leaving 8363 patients who became our study cohort. A prediction model was developed to identify variables that best predicted the risk of developing severe renal insufficiency using multiple logistic regression, and the predictive ability of the model quantified using a bootstrap validated C-Index (Area Under ROC) and Hosmer-Lemeshow statistic. Three percent of the patients with normal renal function before CABG surgery developed severe renal insufficiency (229/8363). In a multivariable model the preoperative patient characteristics most strongly associated with postoperative severe renal insufficiency included: age, gender, white blood cell count >12,000, prior CABG, congestive heart failure, peripheral vascular disease, diabetes, hypertension, and preoperative intraaortic balloon pump. The predictive model was significant with chi2 150.8, probability value <0.0001. The model discriminated well, ROC 0.72 (95%CI: 0.68 to 0.75). The model was well calibrated according to the Hosmer-Lemeshow test.
Conclusions: We developed a robust prediction rule to assist clinicians in identifying patients with normal, or near normal, preoperative renal function who are at high risk of developing severe renal insufficiency. Physicians may be able to take steps to limit this adverse outcome and its associated increase in morbidity and mortality.