A Sliding Scale AdaptiVe Expedited Rescue Algorithm for Deceased Donor Kidney Transplantation

Why It Matters

Reducing kidney nonuse directly expands the donor pool, accelerating transplants and lowering wait‑list mortality. The algorithm offers a scalable, data‑driven tool for transplant networks seeking efficiency without abandoning equity principles.

Key Takeaways

• Nonuse of KDPI>0.35 kidneys fell from 38% to 19%
• Overall kidney nonuse dropped from 28% to 14% with algorithm
• Batch size increase most effective for high KDPI deciles
• Algorithm adapts trigger thresholds based on real-time nonuse rates
• Simulation used KSIM 2.0 and OPTN data across 20 runs

Pulse Analysis

The United States faces a chronic shortage of viable donor kidneys, and recent data show a troubling rise in organ nonuse. Sequential allocation, mandated by the National Organ Transplantation Act, often prolongs cold‑ischemic time, especially for kidneys with higher Kidney Donor Profile Index (KDPI) scores. Prolonged preservation diminishes graft quality, prompting transplant centers to decline organs that might otherwise be salvaged. This inefficiency not only inflates wait‑list times but also drives up healthcare costs associated with dialysis and delayed transplantation.

The Sliding Scale AdaptiVe Expedited rescue algorithm tackles the problem by linking a dynamic rescue trigger to the observed nonuse rate. When the threshold is breached, kidneys are released in sequential batches sized proportionally to the current nonuse metric. Using the KSIM 2.0 simulation platform, researchers ran 20 scenarios with Organ Procurement and Transplantation Network data before and after the Kidney Allocation System 250 overhaul. Results consistently showed a halving of overall nonuse—from 28% down to 14%—and a dramatic cut in high‑KDPI (>0.35) organ discard, dropping from 38% to as low as 19%. Larger batch allocations proved especially potent for the highest KDPI deciles, where traditional allocation lagged.

For transplant programs and policymakers, the algorithm offers a pragmatic, evidence‑based pathway to boost organ utilization without abandoning the fairness of sequential allocation. By shortening cold‑ischemic intervals and rescuing kidneys that would otherwise be discarded, the model promises to increase transplant volumes, improve patient survival, and reduce long‑term dialysis expenditures. Future work will focus on real‑world pilots, integration with existing allocation software, and assessing the impact on post‑transplant outcomes, positioning the approach as a potential new standard in kidney allocation strategy.