A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility

Abstract

Background The Kidney Failure Risk Equation (KFRE) predicts the 2- and 5-year risk of end-stage renal disease (ESRD) in patients with chronic kidney disease (CKD) stages 3a-5. Its predictive performance in advanced CKD and in specific disease aetiologies requires further exploration. This study validates the 4- and 8-variable KFREs in an advanced CKD population in the United Kingdom by evaluating discrimination, calibration and clinical utility. Methods Patients enrolled in the Salford Kidney Study who were referred to the Advanced Kidney Care Service (AKCS) clinic at Salford Royal NHS Foundation Trust between 2011 and 2018 were included. The 4- and 8-variable KFREs were calculated on the first AKCS visit and the observed events of ESRD (dialysis or pre-emptive transplantation) within 2- and 5-years were the primary outcome. The area under the receiver operator characteristic curve (AUC) and calibration plots were used to evaluate discrimination and calibration respectively in the whole cohort and in specific disease aetiologies: diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, autosomal dominant polycystic kidney disease (ADPKD) and other diseases. Clinical utility was assessed with decision curve analyses, comparing the net benefit of using the KFREs against estimated glomerular filtration rate (eGFR) cut-offs of < 20 ml/min/1.73m2 and < 15 ml/min/1.73m2 to guide further treatment. Results A total of 743 patients comprised the 2-year analysis and 613 patients were in the 5-year analysis. Discrimination was good in the whole cohort: the 4-variable KFRE had an AUC of 0.796 (95% confidence interval [CI] 0.762–0.831) for predicting ESRD at 2-years and 0.773 (95% CI 0.736–0.810) at 5-years, and there was good-to-excellent discrimination across disease aetiologies. Calibration plots revealed underestimation of risk at 2-years and overestimation of risk at 5-years, especially in high-risk patients. There was, however, underestimation of risk in patients with ADPKD for all KFRE calculations. The predictive accuracy was similar between the 4- and 8-variable KFREs. Finally, compared to eGFR-based thresholds, the KFRE was the optimal tool to guide further care based on decision curve analyses. Conclusions The 4- and 8-variable KFREs demonstrate adequate discrimination and calibration for predicting ESRD in an advanced CKD population and, importantly, can provide better clinical utility than using an eGFR-based strategy to inform decision-making.

Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease

“…We used the updated 4- and 8- variable KFRE ( 13 ) to predict ESKD within 2 years for the test cohort of 49 patients, however as these equations rely on serum albumin tests, 3 patients had to be excluded ( Table 4 ). The 4-variable KFRE (91.3% accuracy, 25% sensitivity, 97.6% specificity, 50% precision) performed better than the 8-variable KFRE (89.1% accuracy, 0% sensitivity, 97.6% specificity, 0% precision), which was consistent with validation studies ( 27 ), but both were inferior to our model.…”

Contemporary risk prediction models in chronic kidney disease: when less is more

“…An interesting observation was the consistent underestimation of risk in patients with ADPKD. This finding was replicated in a similar cohort of patients with advanced CKD from the United Kingdom [9], indicating that the mechanism of disease progression in ADPKD is driven by factors other than GFR and albuminuria [10,11].…”

CHA2DS2VASc score predicts risk of end stage renal disease in patients with atrial fibrillation: Long-term follow-up study

“…There were many scores (Framingham Heart Study, Simple Risk Score, Best-fit Risk Score, Kidney failure risk equations (KFREs)) published for predicting CKD and renal failure in general populations [ 5 , 7 , 28 , 29 ]. ( Table 4 ).…”