Predictive Models for the Functional Recovery of Transplanted Kidney

Area of Science:

• Nephrology
• Transplant Surgery
• Medical Informatics

Background:

• Delayed graft function (DGF) complicates 20% of renal transplants, impacting outcomes.
• Predictive models for DGF are crucial for optimizing patient management.
• Tailored immunosuppressive therapy can improve post-transplant outcomes.

Purpose of the Study:

• To systematically review and validate DGF predictive scoring systems.
• To identify the most accurate DGF prediction tool for clinical application.
• To enable personalized immunosuppressive strategies based on DGF risk.

Main Methods:

• Systematic literature review to identify DGF scoring systems.
• Application and comparison of identified scores in a cohort of 247 renal transplants.
• Statistical analysis including receiver operating characteristic (ROC) curves.

Main Results:

• Seven DGF scoring systems were identified; three were applicable to the study cohort.
• DGF occurred in 15.95% (41/247) of patients.
• The Irish score demonstrated the highest accuracy (ROC analysis) in predicting DGF, identifying 60.98% of cases, outperforming Jeldres and Chapal scores.

Conclusions:

• The Irish score is the most reliable predictor of DGF among the evaluated systems.
• Accurate DGF prediction is essential for developing targeted therapeutic algorithms.
• This facilitates personalized immunosuppression for renal transplant recipients at higher DGF risk.