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Derivation and validation of a disease-speciﬁc risk score for cardiac risk stratiﬁcation in chronic kidney disease

Objective. Cardiac events (CE; cardiac death, non-fatal myocardial infarction and acute coronary syndrome) are the principal causes of death in patients with chronic kidney disease (CKD). We sought to devise and validate a cardiac risk score to risk-stratify patients with CKD. Methods. Clinical history and biochemical data were obtained in 167 CKD patients. CE were recorded over a median follow-up of 22 months. The hazard ratio (HR) of each independent variable using Cox regression analysis was used to derive a cardiac risk score for the prediction of events. The cardiac risk score was then applied to a validation population of 99 CKD patients to conﬁrm its validity in predicting CE. Results. CE occurred in 20 patients in the derivation group. The independent predictors of CE were cardiac history (HR 9.83, P ¼ 0.001), body mass index (BMI; HR 1.15, P ¼ 0.002), dialysis duration (HR 1.24, P ¼ 0.004) and serum phosphate (HR 4.29, P ¼ 0.001). The resulting cardiac risk score (range 26–67) gave an area under the receiver operating characteristic curve of 0.86. CE occurred in 25 patients in the validation group; the ROC curve area was similar (0.84, P ¼ 0.11). An optimal cardiac risk score cut-off of 50 assigned high risk to 29% of the derivation and 35% of the validation group ( P ¼ 0.26). CE occurred in 35 and 57% of the high-risk derivation and validation groups, respectively ( P ¼ 0.09), and in 2 and 8% of the low-risk groups ( P ¼ 0.15). Conclusion. Application of a cardiac risk score using cardiac history, dialysis duration, BMI and phosphate identiﬁes CKD patients at risk of future CE. GFR DM, PVD, cerebrovascular disease, hypertension, hyperlipidaemia, smoking status, family history of cardiovascular disease or hyperlipidaemia, systolic blood pressure, diastolic blood pressure, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, calcium, phosphate, albumin, haemo-globin, homocysteine, CRP, parathyroid hormone and medications (aspirin, statins, b -blockers, calcium channel blockers and ACE inhibitors). Variables with a P -value <0.1 on univariate analysis were then entered into a multivariate regression analysis and the ﬁnal multivariate model was obtained using a stepwise backwards elimination procedure of variables with a P -value >0.05. A CRS was calculated for each patient in the derivation population using the HRs for each clinical variable in the multivariate model. A receiver operator characteristic (ROC) analysis was performed to determine the best cut-off attributing high risk and low risk of CE. A Cox proportional hazards model was used to compare events between the high- and low-risk groups in the derivation population. Using the same HRs and clinical variables, each patient in the validation population was attributed a CRS and, using the same cut-off of 50, patients were stratiﬁed into a high-risk group or a low-risk group. Comparison between the high- and low-risk groups in the validation population, and then between each risk level in the different populations was made using the Cox model.