Adding biomarker change information to the kidney failure risk equation improves predictive ability for dialysis dependency in eGFR

Abstract

Although the kidney failure risk equation (KFRE), a well-known predictive model for predicting dialysis dependency, is useful, it remains unclear whether the addition of biomarker changes to the KFRE model in patients with an estimated glomerular filtration rate (eGFR) <30ml/min/1.73 m2 will improve its predictive value. We retrospectively identified adults with eGFR <30ml/min/1.73 m2 without dialysis dependency, and available health checkup data for two successive years using a large Japanese claims database (DeSC, Tokyo, Japan). We dichotomized the entire population into a training set (50%) and a validation set (the other half). To assess the incremental value in the predictive ability for dialysis dependency by the addition of changes in eGFR and proteinuria, we calculated the difference in the C-statistics and net reclassification index (NRI). We identified 4499 individuals and observed 422 individuals (incidence of 45.2 per 1000 person-years) who developed dialysis dependency during the observation period (9343 person-years). Adding biomarker changes to the KFRE model improved C-statistics from 0.862 to 0.921, with an improvement of 0.060 (95% confidence intervals (CI) of 0.043-0.076, P<.001). The corresponding NRI was 0.773 (95% CI: 0.637-0.908), with an NRI for events of 0.544 (95% CI of 0.415-0.672) and NRI for non-events of 0.229 (95% CI of 0.186-0.272). The KFRE model was improved by incorporating yearly changes in its components. The added information may help clinicians identify high-risk individuals and improve their care.