Objective Lab-Based Score Could Reshape Heart Transplant Priority and Save More Lives

Every year in the United States, thousands of people with failing hearts wait for a transplant that may never come. Donor hearts are so scarce that roughly one in five patients on the national waiting list dies or is removed from the list before a suitable organ becomes available. That stark arithmetic has long driven transplant researchers to ask a deceptively simple question: are we giving the hearts we do have to the patients who need them most? A new study published in JAMA by researchers at the University of Chicago, working in collaboration with federal policymakers, offers a provocative answer. The team proposes replacing the current heart allocation framework, which ranks candidates largely by the treatments they receive, with a scoring system built on objective, laboratory-based measures of how sick a patient actually is.

The proposed system centers on the U.S. Candidate Risk Score, or US-CRS, a measure first described by the same research group in a 2024 JAMA paper. The score is designed to predict the likelihood that a patient will die within six weeks of being placed on the waiting list if they do not receive a transplant. Rather than relying on which devices or therapies a patient happens to be given, the US-CRS incorporates clinical and laboratory measurements associated with end-stage heart failure, including blood levels of molecules that reflect heart, liver, and kidney dysfunction. The result, the researchers argue, is a more faithful portrait of medical urgency, one that is harder to game and better aligned with the goal of saving the most lives.

To test how the new score would perform in practice, the team analyzed more than 45,000 match runs, the computational exercises that generate ranked lists of transplant candidates whenever a donor heart becomes available. By replaying these allocation decisions under both the current system and the proposed policy, the researchers could compare how well each approach prioritized the sickest patients. The findings were striking: the lab-based scoring system did a better job of identifying the candidates most in need of transplant than the existing six-status model, which sorts patients into tiers of severity based on the treatments they are receiving, such as mechanical circulatory support devices or intra-aortic balloon pumps.

The problem with treatment-based ranking, according to senior author William F. Parker, MD, PhD, Associate Professor of Medicine and Public Health Sciences at the University of Chicago, is that treatment choices are inherently subjective. In the current system, physicians decide which therapies a patient receives, and those decisions directly determine the patient’s priority status. This creates what Parker describes as a natural incentive for well-meaning transplant physicians to treat their patients with more aggressive interventions than they might otherwise choose, simply to move them higher on the list. The system also permits physicians to file exception requests for higher status, giving clinicians additional room to negotiate their patients’ way up the queue.

None of this implies bad faith on the part of transplant cardiologists, who are often making wrenching decisions under enormous time pressure for patients who may die without an organ. But when allocation rules reward the choice of therapy rather than the severity of illness, the rules themselves shape clinical behavior. Patients at centers with more aggressive practices may gain an advantage over equally or more ill patients elsewhere. The aggregate effect is a waiting list that reflects, at least in part, institutional style and physician judgment rather than a consistent, measurable standard of medical urgency. For a national system allocating an irreplaceable and desperately scarce resource, that inconsistency carries a real cost in lives.

The path from a statistical model to a workable national policy is rarely straightforward, and the research team emphasized that collaboration was central to their effort. The scientists partnered with the Scientific Registry of Transplant Recipients, a national database of transplant statistics and analyses operating under the Health Resources and Services Administration, and with the heart committee of the Organ Procurement and Transplant Network, the body that sets organ allocation policy in the United States. Grace Lyden, PhD, an SRTR contract biostatistician and first author of the study, led improvements to the original US-CRS by incorporating more advanced measures of heart function, more detailed treatment information, and refined statistical methods.

Crucially, the team did more than refine a risk score. They built a complete heart allocation algorithm that could be adapted into policy, one that accounts for additional factors that any real-world system must handle, including the geographic distance between the donor hospital and the candidate. In their match run analysis, the proposed policy not only prioritized sicker candidates more effectively but also reduced the distance that donor hearts needed to travel. Shorter transport times can mean healthier organs at implantation and faster transplants for the patients who receive them, so the model’s gains on both urgency and logistics together strengthen the case that the policy could save more lives than the status quo.

The technical logic behind the US-CRS reflects a broader shift in transplant medicine toward data-driven urgency assessment. Blood biomarkers of end-organ failure, such as those signaling cardiac, hepatic, and renal deterioration, capture physiological decline in a way that treatment categories cannot, because the same therapy may be applied to patients at very different stages of illness. By anchoring priority to predicted short-term mortality without transplant, the score aims to allocate each donor heart to the candidate for whom the organ makes the greatest difference in survival. That framing also gives policymakers a quantifiable benchmark: if the score accurately predicts six-week mortality, then ranking candidates by the score directly maximizes the number of lives saved per organ transplanted.

Significant hurdles remain before the US-CRS could become national policy. The score must clear additional administrative review, undergo further testing, and be revised in response to feedback from clinicians, patients, and regulators. Allocation changes in transplantation are high-stakes and closely scrutinized, since any new rule redistributes access to a life-saving resource. Parker said he is nonetheless encouraged by the progress the team has made in refining and validating the model over the past several years, and he credited the direct collaboration with policymakers for providing critical feedback about turning the research into a practical reality.

For the patients and families touched by heart failure, the stakes of this policy debate are starkly concrete. Parker’s stated hope is that the system could be implemented within a few years, so that fewer patients are lost while waiting and the allocation process no longer depends on what he calls arbitrary subjective decision making, but instead reflects the true medical urgency of each candidate. If the match run analysis holds up under further scrutiny, the United States could move toward a heart transplant system in which the sickest patients, measured by the chemistry of their failing organs rather than the devices attached to them, stand first in line for the hearts that become available. In a field where one in five waiting-list patients may not survive long enough to receive a transplant, even a modest improvement in how organs are matched to need could translate into a meaningful number of lives saved.

Subject of Research: A laboratory-based risk score for prioritizing heart transplant candidates by medical urgency

Article Title: New policy could save more lives by prioritizing heart transplant candidates based on medical urgency, not treatment choices

Article References: New policy could save more lives by prioritizing heart transplant candidates based on medical urgency, not treatment choices. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: heart transplant, organ allocation, US-Candidate Risk Score, medical urgency, JAMA, University of Chicago, match run analysis, OPTN, SRTR, heart failure, transplant policy, biomarkers