A-064 Demonstrating statistical process control of a multiplex assay for novel prognostic biomarkers incorporated in the kidneyintelX.dkd test: an FDA-approved test for assessment of risk of progression of diabetic kidney disease

Abstract Background Diabetic Kidney Disease (DKD) is a severe complication of diabetes leading to Kidney Failure in about 5-15% of cases. The kidneyintelX.dkd test is an FDA-approved prognostic test (DEN200052) for assessing the risk of DKD progression, enabling a more personalized approach to treatment and care for those individuals based on risk. The test combines inputs from novel prognostic biomarkers Tumor Necrosis Factor Receptors (TNFR)-1 and TNFR-2 and Kidney Injury Molecule 1 (KIM-1) with readily available clinical laboratory values Blood Urea Nitrogen (BUN), Hemoglobin A1c (HbA1c) and urinary Albumin Creatinine Ratio (UACR). The test reports a low, moderate, or high risk for either a sustained 40% decline in eGFR or kidney failure over 5 years. An analytically validated multiplex electrochemiluminescence assay is used for the in vitro quantitative determination of the novel markers in human plasma (whole blood K2EDTA) on the MESO® SECTOR S 600 instrument. The kidneyintelX.dkd test has been implemented in clinical practice and a comprehensive internal quality control program has been established to assure test performance and acceptability of patient results. In this analysis we present data demonstrating the process control of the assay when deployed in routine laboratory and clinical practice, reflecting multiple sources of process variability over time. Methods This analysis included N=102 kidneyintelX.dkd assay runs performed between April’24 and February’25 by multiple trained operators using various qualified Instruments and combinations of reagent lots. The production lot-numbers of multiplex plates, calibrators and quality control materials were common across all runs. The run-to-run quality control program includes four quality controls for each of the three multiplexed biomarkers (or analytes), namely: high, medium and low diluent-based controls with spiked analyte to known concentrations, and a normal healthy patient pooled plasma-based control. These controls are included in each run for 12 individual quality control criteria per run. We tested statistical outliers, conducted the Shapiro-Wilk test for normality, established the observed mean and ±3 Standard Deviation (SD) ranges for each control/analyte. We plotted Levey-Jennings charts and Box-Whisker plots using Analyse-it software (build 8349.32216) for visualization. Finally, we assessed excursions outside the ±3SD range for each analyte as a percentage of total observations. Results In each case, the distribution of observed values followed a normal distribution on removal of statistical outliers (n=21) or 1.7% of 1224 opportunities for quality control excursions (102 run x 12 QC Criteria). N=9 samples exceeded the ±3SD limits (99.3% within limits), correlating closely with the normal distribution parameters whereby 99.7% of observations are expected to fall within ±3SD of the mean. In each case these had been identified as statistical outliers. The median and interquartile range (IQR) for each control showed consistent concentration of the control recovery around the statistical mean indicating excellent run-to-run consistency (see table of results and example of normal plasma control plots in figure 1). Conclusion The kidneyintelX.dkd assay has been successfully implemented into a routine clinical laboratory workflow and performs consistently and reliably, within statistical process control, over time.