Abstract
Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).
Highlights
Chronic kidney disease (CKD) is a major health and economic burden worldwide, includingAustralia [1,2]
Biomarkers that were associated with progressive CKD in the CKD Queensland (CKD QLD) cohort, in addition to biomarkers identified in the literature, were included in the predictive model development with the CKD Biobank cohort
The aim was to develop a model for accurately predicting progressive CKD
Summary
Chronic kidney disease (CKD) is a major health and economic burden worldwide, includingAustralia [1,2]. There are no clinically robust biomarkers to predict progressive CKD. Biomedicines 2020, 8, 606 to identify progression [4] These traditional biomarkers have the limited predictive capacity for progressive CKD [5]. Lower eGFR and elevated ACR are associated with an increased risk of kidney failure, these biomarkers do not adequately predict worse clinical outcomes in patients with minimal kidney damage or reduced kidney function [6]. CKD patients can display one of several non-linear eGFR trajectories, including stable or increasing eGFR, thereby reducing the predictive utility of longitudinal kidney measurements [7,8]. There have been several attempts to create a model for predicting worse clinical outcomes, such as kidney failure and progressive CKD, with the most notable and robust being The Kidney Failure Risk Equation [9]
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