Combination Automated Microfluidics Measurement of Urine C-C Motif Ligand 2, CXC-Motif Chemokine 9, CXC-Motif Chemokine 10, and Vascular Endothelial Growth Factor A for Monitoring Patients with a Kidney Transplant

Key Points Combining urine C-C motif ligand 2, CXC-motif chemokine 9, CXC-motif chemokine 10, and vascular endothelial growth factor A identifies stable transplant recipients without biopsy-proven acute rejection with >75% specificity and 94% negative predictive value. Measuring four urine analytes in combination using an automated platform is highly efficient (<70 minutes) and reproducible across three independent sites. Automated urine analyte measurement provides critical decision support and outperforms eGFR measurements alone for post-transplantation monitoring. Background Recent studies indicate that up to 36% of pediatric and adult kidney transplant recipients with stable serum creatinine levels will have acute rejection detected on surveillance biopsy. The purpose of this study was to develop and validate a risk algorithm for identifying low- and high-risk patients using a novel automated platform that simultaneously measures urinary C-C motif ligand 2 (CCL2), CXC-motif chemokine 9 (CXCL9), CXC-motif chemokine 10 (CXCL10), and vascular endothelial growth factor A (VEGF-A) with high precision. Methods We designed a multicenter observational study to evaluate the performance of urinary CCL2, CXCL9, CXCL10, and VEGF-A in a training set of 517 banked samples collected at the time of surveillance or indication kidney biopsies from both adult and pediatric recipients. Risk algorithms combining all four analytes were developed in the training set and subsequently validated in three laboratory sites in two additional pediatric cohorts ( N =174). Results The automated platform had remarkably high throughput, generating reproducible results in 60–70 minutes. Analysis was initially performed in the training set ( N =517), which included biopsies read as normal ( N =330), acute rejection ( N =92), or borderline rejection ( N =95). We found that each biomarker independently discriminated normal biopsies versus those with acute rejection ( P < 10 −5 ). A risk algorithm using all four biomarkers (score4) had excellent diagnostic performance for acute rejection in both for-cause and surveillance biopsies performed on patients with stable GFRs, outperforming any individual biomarker as well as estimated GFR assessments. Validation assays performed in the two additional pediatric cohorts in three laboratory sites demonstrated a robust correlation of results; score4 retained excellent diagnostic performance (75% specificity and 92% negative predictive value). Conclusions Automated measurements of urine CCL2, CXCL9, CXCL10, and VEGF-A can distinguish kidney transplant recipients at low versus high risk of rejection. We suggest that this assay can advantage clinical decision making in routine post-transplant monitoring because of its low cost, rapid throughput, and operator independence.