A Sliding Scale AdaptiVe Expedited rescue algorithm for deceased donor kidney transplantation

Area of Science:

• Nephrology
• Transplantation Surgery
• Organ Allocation Policy

Background:

• The nonuse rate of deceased donor kidneys is rising.
• Current organ allocation policies (NOTA) can be inefficient for complex organs, increasing cold ischemic time and organ decline.

Purpose of the Study:

• To develop and evaluate a novel algorithm, the Sliding scale AdaptiVe Expedited (SAVE) Rescue algorithm, to reduce deceased donor kidney nonuse.
• To assess the algorithm's impact on organ utilization across different kidney donor profiles (KDPI strata).

Main Methods:

• The SAVE Rescue algorithm was developed, utilizing nonuse rate to trigger sequential batch allocation.
• The algorithm was simulated using KSIM 2.0 with pre- and post-KAS250 OPTN data across 20 runs.
• Evaluated impact of varying thresholds and batch sizes on nonuse rates, particularly for high KDPI kidneys.

Main Results:

• Overall kidney nonuse decreased from 28% to 14%.
• Nonuse of kidneys with KDPI > 0.35 reduced from 38% to 19%.
• Lower thresholds and larger batch sizes progressively decreased nonuse rates for KDPI > 0.65 kidneys.

Conclusions:

• The SAVE Rescue Algorithm significantly reduces deceased donor kidney nonuse.
• The algorithm enhances organ utilization while maintaining sequential allocation principles.
• Optimized batching strategies are most effective for higher KDPI kidneys.