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

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.