Back

United Network for Organ Sharing Policy Change Impact on Competing Risks in Heart Transplantation: Landmark Analysis of Waitlist Outcomes over Time

Wolfson, A. M.; Chen, M. S.; Ferrall, J.; Kermanshahchi, J.; Fong, M.; Nattiv, J.; Stachel, M.; Kawaguchi, E. S.; Vaidya, A.

2026-09-30 transplantation
10.64898/2026.09.29.26362912 medRxiv
Show abstract

Background Studies of the 2018 United Network for Organ Sharing (UNOS) policy revision suggest improved early transplantation access for the sickest patients. It remains unknown whether these benefits vary as a function of time on the waitlist. Thus, we evaluated potential time-dependent effects of the 2018 heart allocation policy revision on waitlist outcomes. Methods Using UNOS data, we analyzed adult heart transplant candidates listed five years before (pre-policy) and after (post-policy) policy change, categorized as high-acuity (Status 1-3 or 1A) or low-acuity (Status 4-6 or 1B/old 2). Landmark analysis at weeks 0, 1, 2, 4, 8, 16, 24 using a competing risks framework was performed to assess transplantation, death/deterioration, and myocardial recovery. Sub-distribution and cause-specific hazard ratios were calculated. Absolute number and proportion of far-away transplants (donor distance [≥]500 nautical miles) were calculated at each timepoint. Results High-acuity patients experienced significantly higher risk of transplantation within 2 weeks post-policy change but equivocal risk by week 16. Low-acuity patients exhibited increased rate/risk of myocardial recovery and equal or improved death/deterioration risk at all timepoints; early transplantation cumulative incidence was increased. At all timepoints, both groups experienced significantly increased far-away donor use, highest immediately after listing and progressively declining over time. Conclusions The 2018 heart allocation policy change resulted in an initial appearance of time-dependent benefits among high-acuity, post-policy patients, but this was likely due to rapid transplantation of Status 1 and 2 patients from broader geographic sharing rather than a true temporal phenomenon. Post-policy, low-acuity patients experience benefits in all waitlist outcomes.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.