*Purpose: A recent cohort study of 10 869 living kidney donors from ImmPort highlighted that 14.7% have postdonation events unto 40 yrs later, relating to increased risk of CKD (Chen, 2019), highlighting the need to develop improved methods to track renal function and CKD over time.

*Methods: We developed a novel kidney function score, ueGFR, based on urinary markers total protein, creatinine, and DMAIMO, and trained a model score to evaluate renal function, as measured by eGFR (CKD-EPI) based on paired blood and urine sampling. The model was trained to detect various stages of CKD, without race imputation, and correlation analysis performed between ueGFR and eGFR. Non-parametric Mann-Whitney-U with Holm-Bonferroni correction for multiple testing were conducted to determine CKD staging, and Kruskal-Wallis with Dunn’s post-hoc multiple testing correction were conducted to determine global statistical differences in ueGFR and eGFR values across all CKD stages.  High-quality synthetic samples were generated for algorithm training in order to increase statistical robustness to system-noise and accurately predict CKD stage.

*Results: ueGFR score has a high correlation with the CKD-EPI eGFR (Spearman r2>0.90 across all eGFR formulas). The ueGFR score was validated to discriminate different stages of CKD as determined by the eGFR (Kruskal-Wallis p-value = 1.7e-17). ueGFR (Figure 1) had an overall model accuracy at 92% (95% CI: 85% -96%), and each predicted CKD stage at a balanced accuracy of over 90% (stage 1: 95%, stage 2: 97%, stage 3: 93%, stage 4: 91%, and stage 5: 99%).

*Conclusions: The novel ueGFR assay provides an accurate and non-invasive option for repeated assessment of kidney function and the accurate staging of both early and later stages of CKD and native kidney injury. The inclusion of a sensitive non-invasive urine assay for serial monitoring of native renal function, could increase access to at-home and convenient kidney function assessments in at-risk populations, inclusive of living kidney donors.

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