Where you list for a transplant
shapes your odds. Dramatically.

They are not created equal.

Kidney wait times across U.S. transplant centers range from roughly 8 months at the most favorable programs to over 11 years at the most competitive. The same organ, the same blood type, the same medical urgency — but the choice of center determines when, or whether, a transplant happens.

Two hospitals 40 miles apart can differ by years of expected waiting. A patient listed in Phoenix may be transplanted within months. Their counterpart at a high-volume Boston center, who chose it for its reputation, may wait a decade. This is not a function of how sick they are. It is a function of which program they chose.

Each center is colored from green (shorter waits) to red (longer waits). The gradient is not random. It mirrors regional organ supply, accumulated waitlist pressure, and decades of uneven policy.

Where organs come from shapes everything downstream.

Most transplantable organs come from deceased donors who die suddenly: motor vehicle accidents, strokes, drug overdoses. For a donation to occur, death must be declared, the organ procured, and a compatible recipient reached in hours. Hearts survive roughly 4 to 6 hours outside the body. Kidneys, up to 36.

The red-shaded regions show areas with higher traffic fatality rates, which correlate with higher potential deceased donor supply. But high-fatality areas rarely overlap with major transplant centers. This geographic mismatch between where organs become available and where patients wait is one of the central inefficiencies of the current allocation system.

A center's proximity to active organ procurement corridors is a real logistical advantage. Distance from supply means more declined offers, longer cold ischemia times, and lower transplant rates for patients at that program.

The cause of death determines which organs are viable.

Donors declared brain dead (DBD) — typically from trauma — maintain circulation until the moment of procurement. This preserves kidneys, livers, and pancreases well, and yields the highest-quality hearts and lungs. Donors declared dead by cardiac criteria (DCD) experience a period of warm ischemia before procurement, which limits heart and lung viability but still produces viable kidneys and, increasingly, livers.

A region with high traffic fatalities may generate strong DBD kidney and liver supply but offer fewer viable hearts or lungs. Rural areas with high DCD rates may supply kidneys to nearby centers but be unable to serve a patient awaiting a heart transplant 200 miles away.

Not every eligible death produces a donor.

Registered donors must be identified among the eligible deceased, and family consent obtained. Registration rates across U.S. states range from roughly 35% to 68% of licensed drivers. High-registration states benefit from a deeper potential donor pool and, typically, shorter wait times. Low-registration states rely more on family authorization at the bedside, a harder and less consistent process.

Beyond registration, organ procurement organizations (OPOs) vary widely in how effectively they convert potential donors into actual ones. Some OPOs operate at the top of their potential; others leave significant supply unrealized. The blue-shaded regions reflect state-level registration density. This is the upstream input that shapes everything a transplant center can offer its patients.

When you layer supply patterns, registration rates, hospital volume, and wait times together, the picture of why where you list matters begins to sharpen into something unavoidable.

Monte Carlo simulation with competing risks.

For each of the 248 centers, we run 1,000 simulation iterations modeling the probability of receiving a transplant at 6, 12, 24, and 36 months. Each iteration draws on OPTN historical offer and acceptance rates and organ-specific donor flow by region. Critically, each iteration accounts for competing risks: the chance of transplant, the chance of dying while waiting, and the chance of removal for other medical reasons.

A 48-profile demographic matrix runs each center across blood types, age brackets, and sex to evaluate equity. We then compute a Gini coefficient per center, a standard measure of distributional inequity, to identify programs where certain patient profiles are systematically disadvantaged relative to others at the same institution.

50+ variables. 8 weighted categories. One score.

transplant.today synthesizes all of these layers into a composite suitability score for each center and organ type. The score draws on medical compatibility, wait-time probability, donor supply quality, hospital outcomes (CMS), logistical access, chronic disease burden (CDC PLACES), air quality (EPA AQS), and socioeconomic support (BLS).

The weights are not fixed. They adapt based on organ type, blood type, urgency tier, and clinical parameters. A heart patient's score weights heavily toward donor quality and center volume. A kidney patient's may weight more toward wait-time probability and equity. Larger, brighter markers on this map indicate higher composite scores for a default kidney profile.

A tool for decisions. A platform for discovery.

For patients and families, the Simulator takes your organ type, blood type, and clinical profile and returns personalized center rankings with wait-time probability forecasts: not just raw wait times, but the actual probability you will be transplanted within 6, 12, 24, and 36 months at each center given your specific situation. The Center Explorer lets you browse all programs, or use Compare Centers to view any two side by side across every scoring category.

For researchers, clinicians, and policy advocates, the Data Explorer exposes every layer of this model as a toggleable map overlay with real values. Run what-if policy scenarios: what happens to national wait times if OPO performance improves in the South? How does a donor registration drive in a low-registration state affect equity? Export any layer as CSV or JSON for independent analysis.