A new risk score model to predict the presence of significant coronary artery disease in renal transplant candidates

Abstract

BACKGROUND Renal transplant candidates are at high risk of coronary artery disease (CAD). We sought to develop a new risk score model to determine the pre-test probability of the occurrence of significant CAD in renal transplant candidates. METHODS A total of 1,060 renal transplant candidates underwent a comprehensive cardiovascular risk evaluation. Patients considered at high risk of CAD (age ≥50 years, with either diabetes mellitus (DM) or cardiovascular disease (CVD)), or having noninvasive testing suggestive of CAD were referred for coronary angiography (n = 524). Significant CAD was defined by the presence of luminal stenosis ≥70%. A binary logistic regression model was built, and the resulting logistic regression coefficient B for each variable was multiplied by 10 and rounded to the next whole number. For each patient, a corresponding risk score was calculated and the receiver operating characteristic (ROC) curve was constructed. RESULTS The final equation for the model was risk score = (age × 0.4) + (DM × 9) + (CVD × 14) and for the probability of CAD (%) = (risk score × 2) - 23. The corresponding ROC for the accuracy of the diagnosis of CAD was 0.75 (P <0.0001) in the developmental model. CONCLUSIONS We developed a simple clinical risk score to determine the pre-test probability of significant CAD in renal transplant candidates. This model may help those directly involved in the care of patients with end-stage renal disease being considered for transplantation in an attempt to reduce the rate of cardiovascular events that presently hampers the long-term prognosis of such patients.