Combining GFR and albuminuria to classify CKD improves prediction of ESRD

Abstract

Despite the high prevalence of chronic kidney disease (CKD), relatively few individuals with CKD progress to ESRD. A better understanding of the risk factors for progression could improve the classification system of CKD and strategies for screening. We analyzed data from 65,589 adults who participated in the Nord-Trøndelag Health (HUNT 2) Study (1995 to 1997) and found 124 patients who progressed to ESRD after 10.3 yr of follow-up. In multivariable survival analysis, estimated GFR (eGFR) and albuminuria were independently and strongly associated with progression to ESRD: Hazard ratios for eGFR 45 to 59, 30 to 44, and 15 to 29 ml/min per 1.73 m(2) were 6.7, 18.8, and 65.7, respectively (P < 0.001 for all), and for micro- and macroalbuminuria were 13.0 and 47.2 (P < 0.001 for both). Hypertension, diabetes, male gender, smoking, depression, obesity, cardiovascular disease, dyslipidemia, physical activity and education did not add predictive information. Time-dependent receiver operating characteristic analyses showed that considering both the urinary albumin/creatinine ratio and eGFR substantially improved diagnostic accuracy. Referral based on current stages 3 to 4 CKD (eGFR 15 to 59 ml/min per 1.73 m(2)) would include 4.7% of the general population and identify 69.4% of all individuals progressing to ESRD. Referral based on our classification system would include 1.4% of the general population without losing predictive power (i.e., it would detect 65.6% of all individuals progressing to ESRD). In conclusion, all levels of reduced eGFR should be complemented by quantification of urinary albumin to predict optimally progression to ESRD.

Figure 1.

HR (95% CI) for ESRD associated with urine albumin excretion. The fractional polynomial analysis was adjusted for age, gender, and eGFR, and the reference (HR 1) was set to the median ACR (8.6 mg/g).

Figure 2.

Diagnostic accuracy of prognostic markers for future ESRD in the general population. The ROC curves show true-positive (TP) rates (i.e., sensitivity) for all possible false-positive (FP) rates (i.e., 1 − specificity). Increasing AUC indicates better diagnostic accuracy, and the clinical relevant region of the curve is at low FP rates (<0.10).