INTRODUCTION:Following implementation of the U.S. Kidney Allocation System (KAS) in 2014, deceased donor kidneys with a kidney donor profile index (KDPI) < 35% are prioritized for allocation to pediatric candidates. Early post-KAS data suggested this prioritization may have led to more frequent delayed graft function compared to pre-KAS, when pediatric allocation priority was based on donor age < 35 years. We sought to understand the impact of this allocation change on longer-term pediatric kidney transplant outcomes. METHODS:We used SRTR data to identify all deceased donor kidney transplants with pediatric recipients during two eras: "Pre-KAS" (12/1/2009-11/30/2014) and "KAS" (12/1/2015-11/30/2020). We used Cox proportional hazards models to calculate the association between study era and all-cause graft failure (graft failure or death) after adjusting for recipient characteristics. RESULTS:Among 4502 included transplants, 2175 (48%) were in the pre-KAS era and 2327 (52%) in the KAS era. KAS-era donors were older (median age 23 years, 13% age ≥ 35 years vs. median age 21, 1% age ≥ 35 years), less likely to have diabetes and hypertension, and had lower serum creatinine. Transplantation during the KAS era was associated with a lower hazard of graft failure after adjusting for recipient characteristics (adjusted HR 0.690.790.91, p = 0.001). Results were similar in sensitivity analyses limited to recipients < 10 years old and recipients alive with a functioning graft 90 days post-transplant. CONCLUSIONS:KDPI-based prioritization of kidneys for pediatric allocation was associated with a lower risk of graft failure compared to donor age-based prioritization. Further refining donor risk scores may enable additional improvements in graft survival.

SUMMARY:Since their early development in the 1980s, Simulated Allocation Models (SAMs) have helped policymakers forecast the impact of proposed allocation policy changes on patient outcomes before implementation. In the United States, models like the Kidney-Pancreas Simulated Allocation Model, Liver Simulated Allocation Model, and Thoracic Simulated Allocation Model have been instrumental in shaping organ allocation policies. Analogous models have emerged globally, including the ETKidney and Eurotransplant Liver Allocation System simulators for the Eurotransplant region, to address country and region-specific allocation challenges. This review categorizes and compares SAMs based on their core assumptions, data, and modeling approaches. We highlight challenges in model validation, the use of synthetic data, and model transparency. While simplifying assumptions are often necessary because of limited data, their influence on results should be clearly communicated to ensure policymakers can interpret model predictions accurately. Furthermore, model validation using both retrospective and prospective data is essential to assess performance under evolving policies. Greater transparency through open-source models, detailed reporting of assumptions, and validation efforts can enhance collaboration, reproducibility, and confidence in transplant research. By providing a global perspective on SAMs, this review aims to inform future research and policy development, promoting evidence-based policy development in organ transplantation.

Background:Efforts to reduce waitlist mortality in lung transplantation have been hindered by a high donor lung non-use rate, with approximately one-fifth of deceased organ donors ultimately providing lungs for transplantation. Rationale for organ decline varies across individual providers and transplant centers. Greater donor lung utilization could significantly reduce waitlist mortality and improve clinical outcomes for adult lung transplant candidates. Objective:This study aimed to evaluate whether machine learning models can successfully predict donor lung non-use and characterize donor factors associated with current utilization patterns, with the long-term goal of informing more consistent and data-driven donor lung evaluation. Methods:Using U.S. national registry data collected between 2012 and 2022, we generated five machine learning models - logistic regression, decision tree, random forest, XGBoost, and Naive Bayes - all of which were trained with 19 donor variables to predict donor lung non-use. Models were tested with both full donor feature sets and restricted donor feature sets comprised of the top 15, 10, and 5 predictors. Performance metrics, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score, were assessed using cross-validation. Results:The random forest model incorporating the full feature set achieved the highest performance with an AUROC of 0.960 (95% CI 0.958-0.961) and an F1 score of 0.9489. However, the logistic regression (AUROC 0.886, 95% CI 0.883-0.888) and XGBoost (AUROC 0.906, 95% CI 0.902-0.908) models also demonstrated robust performance. A restricted random forest model that included only the top 15 predictors demonstrated similar efficacy (AUROC 0.956, 95% CI 0.954-0.958). Key predictors included PaO2:FiO2 ratio, donor age, body mass index, and serum aspartate aminotransferase levels. More restricted random forest models (i.e., those that only included 5 or 10 predictors) and any of the restricted logistic regression and XGBoost models had inferior but still acceptable performance (AUROC 0.867-0.958). Conclusion:Machine learning models, particularly random forest, XGBoost, and logistic regression, accurately predicted donor lung non-use. Restricted models using fewer predictors also maintained strong performance, demonstrating technical feasibility for future evaluation in time-sensitive workflows. Because the models were trained on historical utilization decisions rather than recipient outcomes or independent measures of donor suitability, prospective validation is needed to determine whether they can improve appropriate donor lung utilization or support acceptance decisions without reinforcing practice-pattern bias.

This study describes secular trends in organ donation after circulatory death in the United States between 2000 and 2025.

Lungs recovered from donation after circulatory death (DCD) are markedly underutilized for transplantation in the U.S. Evidence demonstrating excellent outcomes after DCD lung transplantation underpins the need for strategies to mitigate barriers to DCD lung utilization, including data driven revisions to allocation policy, revising program-specific quality metrics, removing financial barriers, and minimizing logistical disincentives. Expanded DCD transplantation is vital to reduce waitlist mortality and increase lung transplant rate.

BACKGROUND:Machine perfusion in liver transplantation has allowed use of higher-risk donors (older age, higher macrosteatosis, or donation after circulatory death), with improved outcomes compared with static cold storage. Clinical outcomes between the most common machine perfusion modalities (hypothermic and normothermic) have not been reported. STUDY DESIGN:Adult deceased donor liver transplant recipients who received an allograft reported as machine perfused by the organ procurement organization were identified using 2016 to 2024 Organ Procurement and Transplantation Network data in the US. Machine perfusion was categorized as hypothermic (n = 235) or normothermic (n = 3,897). Risks of all-cause and death-censored graft loss were quantified using propensity-weighted Cox proportional hazard models by machine perfusion modality. RESULTS:Machine perfusion of transplanted livers increased 11-fold and 25-fold for hypothermic (2019: n = 9; 2024: n = 109) and normothermic (2019: n = 84; 2024: n = 2,167) perfusion. One-year adjusted risks of all-cause (adjusted hazard ratio 1.00, 95% CI 0.59 to 1.69, p = 0.99) and death-censored (adjusted hazard ratio 1.00, 95% CI 0.43 to 2.35, p = 1.0) graft loss were similar between the 2 machine perfusion types. Additionally, the cumulative incidences of primary nonfunction and hepatic artery thrombosis were similar among liver transplant recipients between hypothermic and normothermic machine perfusion. CONCLUSIONS:This report is the first direct comparison of outcomes among liver transplant recipients by machine perfusion modality. It demonstrates increased use of machine perfusion, and these preliminary findings suggest equivalent outcomes among liver transplant recipients with hypothermic and normothermic machine perfusion on a national level, although further study is needed.

The proportion of deceased donor kidneys recovered for transplantation that are not transplanted reached 28% in 2023. Past research demonstrated that >90% of the nonuse rate (NUR) increase in the 2000s could be explained by the broadening donor pool. We used the Organ Procurement and Transplantation Network data to study kidneys recovered from 2010-2023, applying causal inference methods to assess the degree to which the recent, sharp rise in the NUR could be explained by changes in donor clinical characteristics. Unadjusted odds of kidney nonuse were 63% higher (95% CI: 56%, 70%) in 2023 vs 2018. After adjusting for donor factors, the odds of nonuse were only 12% (9%, 15%) higher in 2023. Both regression and propensity weighting demonstrated that 75% to 80% of the recent NUR increase can be explained by a rapidly expanding donor pool. Encouragingly, the NUR has not increased and remains low for above-average quality kidneys. However, the unexplained risk of nonuse for kidneys in the highest kidney donor risk index quartile increased by ∼ 30%, potentially due to residual confounding and/or system-level, exogenous factors such as allocation policy changes. To improve placement efficiency, allocation policy should adapt to the increasingly heterogeneous donor pool by allocating kidneys differently along the donor quality spectrum.

Liver transplant (LT) recipients experience a wide range of comorbidities, leading to frequent healthcare encounters. Until now, national registries, which have limited exposures and outcomes, and laborious small cohort studies have been the main data sources for LT research. Cosmos database offers electronic health record (EHR)-based insights into LT recipients at the national level with granular data. We evaluated whether Cosmos data is representative of the entire US LT recipient population. Using Cosmos (N=20,235) and the national Scientific Registry of Transplant Recipients (SRTR) (N=51,281), we identified adult, first-time LT recipients between July 2016 and December 2022. We compared demographics, clinical data, and mortality across datasets, calculating Kaplan-Meier survival estimates and multivariable Cox regressions. Recipient characteristics were highly comparable (eg, female: Cosmos=36.5% vs. SRTR=36.4%, Black: 6.8% vs. 7.2%; BMI: 28.5 kg/m 2 [24.8-32.9] vs. 28.2 [24.6-32.4]). Lab values were similar across cohorts, including MELD (24 [17-30] vs. 23 [16-30]). Transplant indications, donor characteristics, and 5-year survival (Cosmos 83.1% [82.3-83.8] vs. SRTR 80.9% [80.4-81.3]) were similar. The associations of clinical factors with survival were similar across both groups. The Cosmos database demonstrated acceptable generalizability to the general US LT recipient population, which may advance LT research through a better understanding of LT recipients' experiences and outcomes.

ABSTRACT Objective Given frailty and comorbidities that occur with both aging and end‐stage kidney disease (ESKD), it is unclear if older patients with ESKD derive the improved survival and kidney transplant (KT) access associated with Roux‐en‐Y gastric bypass (RYGB) or sleeve gastrectomy (SG). Methods Using 2006–2021 USRDS data, we identified 876 patients with RYGB and 1508 patients with SG and compared 5‐year mortality by age‐group (18–29/30–39/40–49/50–59/60–69/≥ 70 years) to nonsurgical matched controls using 1:3 Mahalanobis distance matching, Kaplan–Meier, and Cox regression. We also compared age‐stratified KT incidence between waitlisted patients and controls. Results Among patients with RYGB versus controls, 5‐year mortality was 11.4% versus 17.3% (aHR = 0.23 0.58 1.44 ), 31.5% versus 30.1% (aHR = 0.73 1.02 1.41 ), and 37.9% versus 47.3% (aHR = 0.69 0.77 1.00 ) for 18–29/30–39/40–49 years; however, 5‐year mortality was 77.1% versus 68.3% (aHR = 1.24 1.56 1.95 ) for 60–69 years and 86.8% versus 78.7% (aHR = 1.80 2.39 3.16 ) for ≥ 70 years. Among patients with SG versus controls, 5‐year mortality was 17.8% versus 30.2% (aHR = 0.26 0.47 0.83 ), 18.1% versus 36.3% (aHR = 0.28 0.39 0.53 ), 28.7% versus 48.9% (aHR = 0.35 0.43 0.53 ), 31.1% versus 61.6% (aHR = 0.27 0.35 0.44 ), 37.3% versus 65.7% (aHR = 0.35 0.48 0.66 ), and 51.5% versus 93.6% (aHR = 0.14 0.37 0.94 ) for 18–29/30–39/40–49/50–59/60–69/≥ 70 years. Among listed ≥ 65 years, KT incidence was 21.3% versus 25.4% (aHR = 0.19 1.01 5.26 ) for patients with RYGB versus controls and 66.7% versus 39.9% (aHR = 0.62 2.31 8.64 ) for patients with SG versus controls. Conclusions RYGB in older patients with ESKD is associated with increased mortality and lower KT likelihood, whereas SG is associated with decreased mortality and higher KT likelihood compared to nonsurgical matched controls. Choice of bariatric surgery type may play a role in improving survival for older patients with ESKD.

Background:Patients with pulmonary hypertension (PH) have previously experienced worse waitlist outcomes than peers with other diagnoses. In 2021, the Lung Allocation Score (LAS) was revised to improve the prediction of expected survival. The Composite Allocation Score (CAS) was subsequently implemented in 2023. The effects of these changes on waitlist outcomes for patients with PH are not known. Methods:A retrospective analysis of the United Network for Organ Sharing database was performed in 3 eras: LAS Era 1 (November 24, 2017-September 30, 2021), LAS Era 2 (October 1, 2021-March 8, 2023), and CAS Era (March 9, 2023-June 27, 2024). Unadjusted and adjusted competing risks regression analyzed waitlist outcomes within each era comparing diagnosis groups, and for PH patients across eras. Results:Adjusted waitlist mortality for PH patients was worse relative to chronic obstructive pulmonary disease (COPD) and cystic fibrosis in LAS Era 1, not significantly different from other groups in LAS Era 2, and worse relative to COPD and interstitial lung disease in the CAS Era. Waitlist mortality for PH patients was unchanged between the LAS Eras and the CAS Era. Transplantation rate for PH patients was improved in the CAS Era compared to LAS Era 2, when measures of right heart dysfunction were removed from the LAS calculations, but not compared to LAS Era 1. Conclusion:In the CAS Era, PH patients continue to experience increased waitlist mortality relative to non-PH diagnoses. Waitlist mortality for PH patients has not improved in the CAS Era compared to the LAS Eras.

Despite the high demand, >7500 recovered kidneys annually go unused, with transplant centers showing significant variation in their offer acceptance practices. However, it remains unclear how much of this variation occurs between individual clinicians within the same center and its impact on allocation efficiency and equity. This study quantified the variability in kidney offer acceptance decisions attributable to clinicians vs centers and examined the role of donor quality in acceptance decisions. We analyzed national transplant registry data (from January 2016 to December 2020) linked to on-call records from 15 transplant centers, creating a clinician-level data set with 344 678 deceased donor kidney offers. The primary outcome was the variability in offer acceptance attributable to clinicians vs centers, quantified via hierarchical, mixed-effect logistic regression models. To complement kidney donor profile index as a measure of donor quality, we incorporated expected acceptance probability, adjusting for a broader set of donor characteristics and recipient factors. Both center-level (0.35; 95% CI: 0.15-0.79) and clinician-level (0.10; 95% CI: 0.06-0.18) variances were significant, with heterogeneity in the kidney donor profile index-acceptance association among clinicians. These results underscore the need for further research into the mechanisms driving the clinician-level variation and its implications for organ allocation efficacy, equity, and patient outcomes.