Biomarkers as diagnostic tests for delayed graft function in kidney transplantation

Abstract

Delayed graft function (DGF) after kidney transplantation is associated with inferior outcomes and higher healthcare costs. DGF is currently defined as the requirement for dialysis within seven days post-transplant; however, this definition is subjective and nonspecific. Novel biomarkers have potential to improve objectivity and enable earlier diagnosis of DGF. We reviewed the literature to describe the range of novel biomarkers previously studied to predict DGF. We identified marked heterogeneity and low reporting quality of published studies. Among the novel biomarkers, serum NGAL had the greatest potential as a biomarker to predict DGF, but requires further assessment and validation through larger scale studies of diagnostic test performance. Given inadequacies in the dialysis-based definition, coupled with the high incidence and impact of DGF, such studies should be pursued.

Comprehensive analysis of necroptosis-related genes in renal ischemia-reperfusion injury

“…Despite the improvement of patient and graft survival rates with scientific advances and increasing potency of immunosuppressants, DGF following renal IRI remains an intricate complication after kidney transplantation, resulting in increased morbidity and resource utilization, including longer hospital stays, post-acute care, and higher costs ( 4 , 31 ). Existing biomarkers like neutrophil gelatinase-associated lipocalin (NGAL) and cystatin C are still not widely applied in clinical settings ( 32 ), and proposed models for DGF after deceased-donor transplantation may overestimate its incidence ( 33 ). Necroptosis, a caspase-independent form of RN, has been proven to be associated with various models of renal injury, including the oxidative stress-derived IRI ( 6 ).…”

Identification of potential necroinflammation-associated necroptosis-related biomarkers for delayed graft function and renal allograft failure: a machine learning-based exploration in the framework of predictive, preventive, and personalized medicine

“…Furthermore, some previous predictive models had limited ability to predict DGF [ 20 ]. Indeed, researchers have been constantly trying to find biomarkers to diagnose or predict renal allograft dysfunction, and some of the findings have had varying degrees of success in the preclinical and clinical stages [ 21 , 22 ]. However, there is growing evidence that a perfect single marker may not exist [ 13 ].…”