BACKGROUND:Renal transplant recipients (RTRs) have increased cardiovascular disease (CVD) risk. Standard CVD risk calculators are poorly predictive in RTRs; we therefore aimed to develop and validate an equation for CVD risk prediction in this population. METHODS: We used data from the Assessment of Lescol in Renal Transplantation trial, which are randomly divided into an assessment sample and a test sample (67% and 33%, respectively, of the total population). For variable selection in the assessment sample, backward stepwise Cox regression was used. Using the regression coefficients and centralized prognostic index, risk was calculated for individual patients. The equation was then validated for calibration and discrimination using the test sample. RESULTS: Major adverse cardiac events could be predicted using a seven-variable model including age, previous coronary heart disease, diabetes, low-density lipoprotein, creatinine, number of transplants, and smoking. The calibration of the model was good in the test sample with a Hosmer-Lemeshow chi-square value of 11.47 and a P value of 0.245. The areas under the receiver operating characteristic curve were 0.738 in the assessment sample and 0.740 in the test sample. Total mortality could be predicted using a six-variable model including age, coronary heart disease, diabetes, creatinine, total time on renal replacement therapy, and smoking. The calibration of the model was acceptable in the test sample with a Hosmer-Lemeshow chi-square value of 13.08 and a P value of 0.109. The areas under the receiver operating characteristic curve were 0.734 in the assessment sample and 0.720 in the test sample. CONCLUSIONS: Using the Assessment of Lescol in Renal Transplantation trial population, a formula for 7-year CVD and mortality risk calculation for prevalent RTRs has been developed.
RCT Entities:
BACKGROUND: Renal transplant recipients (RTRs) have increased cardiovascular disease (CVD) risk. Standard CVD risk calculators are poorly predictive in RTRs; we therefore aimed to develop and validate an equation for CVD risk prediction in this population. METHODS: We used data from the Assessment of Lescol in Renal Transplantation trial, which are randomly divided into an assessment sample and a test sample (67% and 33%, respectively, of the total population). For variable selection in the assessment sample, backward stepwise Cox regression was used. Using the regression coefficients and centralized prognostic index, risk was calculated for individual patients. The equation was then validated for calibration and discrimination using the test sample. RESULTS: Major adverse cardiac events could be predicted using a seven-variable model including age, previous coronary heart disease, diabetes, low-density lipoprotein, creatinine, number of transplants, and smoking. The calibration of the model was good in the test sample with a Hosmer-Lemeshow chi-square value of 11.47 and a P value of 0.245. The areas under the receiver operating characteristic curve were 0.738 in the assessment sample and 0.740 in the test sample. Total mortality could be predicted using a six-variable model including age, coronary heart disease, diabetes, creatinine, total time on renal replacement therapy, and smoking. The calibration of the model was acceptable in the test sample with a Hosmer-Lemeshow chi-square value of 13.08 and a P value of 0.109. The areas under the receiver operating characteristic curve were 0.734 in the assessment sample and 0.720 in the test sample. CONCLUSIONS: Using the Assessment of Lescol in Renal Transplantation trial population, a formula for 7-year CVD and mortality risk calculation for prevalent RTRs has been developed.