Laboratory Medicine Approaches for Assessing Delayed Graft Function in Renal Transplant

Area of Science:

• Nephrology
• Transplantation immunology
• Biomarker discovery

Background:

• Delayed graft function (DGF) is a major complication in renal transplantation, impacting graft survival and healthcare costs.
• Optimizing donor organ selection and post-transplant outcomes are critical as kidney transplantation rates increase.
• Early identification and prediction of DGF are essential for timely interventions.

Purpose of the Study:

• To review the causes, risk factors, and consequences of DGF.
• To highlight emerging biomarkers for early DGF detection and risk stratification.
• To explore the role of artificial intelligence in predictive modeling for kidney transplantation.

Main Methods:

• Review of multifactorial causes and determinants of DGF (ischemia-reperfusion injury, donor/recipient factors).
• Examination of novel biomarkers: neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, proenkephalin, and interleukin-18.
• Exploration of artificial intelligence integration into predictive models combining diverse variables.

Main Results:

• Emerging biomarkers show promise for early DGF detection and risk stratification.
• AI-driven predictive models can integrate molecular, clinical, and procedural data.
• Improved organ allocation and individualized patient management are potential outcomes.

Conclusions:

• Assessing biomarkers preoperatively in donors can provide earlier insights into organ quality.
• Standardization of biomarker measurement is necessary for reliable prediction.
• Enhanced donor characterization is key for clinical translation of predictive tools.