Faculty Bibliography

BACKGROUND:With a broadening deceased donor pool and recent allocation policy changes, US transplant programs have reported challenges managing an influx of suboptimal kidney offers. We hypothesized that the number of offered kidneys with a minuscule chance of being transplanted has risen. If so, targeted innovations may be needed to reduce administrative burden and improve system efficiency. METHODS:Using Organ Procurement and Transplantation Network data on kidneys offered in 2016-2024, we developed a multivariable model to predict the probability that an offered kidney was transplanted (PrTx). We examined trends in the number and proportion of offered kidneys having PrTx ≤2%, ≤5%, and ≤10%. RESULTS:Donor factors most predictive of PrTx were age, donation after circulatory death, serum creatinine (terminal, peak, nadir), presence and duration of diabetes and hypertension, infectious disease test results, and body mass index. From 2016 to 2024, the annual number of kidneys offered with PrTx ≤2%, PrTx ≤5%, and PrTx ≤10% increased 13-fold (from 18 to 236), 12-fold (84 to 1055), and 10-fold (299 to 2856), respectively. Of the 236 offered kidneys with PrTx ≤2% in 2024, none was transplanted. The 2856 kidneys with PrTx ≤10% accounted for 7.9% (n = 1 208 883) of manually refused offers but just 1.1% (n = 239) of transplanted kidneys. Organ procurement organizations and centers varied substantially in offering and transplanting low-PrTx kidneys. CONCLUSIONS:The number of offered kidneys with extremely low predicted transplant probability has risen sharply, most notably in 2024. Innovations, such as improved offer filtering capabilities and standardized expedited allocation pathways, are needed to improve kidney placement efficiency.

BACKGROUND:Metabolic and bariatric surgery (MBS) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) are effective treatments for obesity and type 2 diabetes, but their comparative survival and waitlisting benefits in end-stage kidney disease (ESKD) remain unclear. OBJECTIVES/OBJECTIVE:To compare survival and kidney transplant (KT) waitlisting after MBS and GLP-1RA use among patients with ESKD, using dipeptidyl peptidase-4 inhibitors (DPP-4is) as an active comparator. SETTING/METHODS:US Renal Data System, 2010-2022. METHODS:We included adults with ESKD, type 2 diabetes, and ≥class II obesity treated with MBS, GLP-1RA, or DPP-4i. Three target trials were emulated: MBS versus GLP-1RA (N = 1818), MBS versus DPP-4i (N = 2111), and GLP-1RA versus DPP-4i (N = 4338). Cox models estimated adjusted hazard ratios (aHRs) for all-cause mortality and KT waitlisting over 1-5 years. RESULTS:MBS recipients were younger at dialysis initiation than matched GLP-1RA and DPP-4i users. MBS was associated with higher short-term mortality than matched GLP-1RA (1-year aHR = 2.68, 95% confidence interval [CI]:1.98-3.63; 3-year aHR = 1.34, 95% CI: 1.09-1.64) and DPP-4i users (1-year aHR = 1.74, 95% CI: 1.35-2.25), but comparable longer-term mortality. GLP-1RA users had consistently lower mortality than matched DPP-4i users. MBS was associated with higher KT waitlisting than GLP-1RA and DPP-4i use, with no differences between GLP-1RA and DPP-4i users. CONCLUSIONS:Among patients with ESKD, type 2 diabetes, and obesity, MBS was associated with higher short-term but comparable long-term mortality, whereas GLP-1RA use was associated with lower mortality than DPP-4i use. MBS was also associated with higher KT waitlisting than GLP-1RA and DPP-4i use, suggesting distinct risk-benefit profiles of metabolic treatment in this high-risk population.

INTRODUCTION:In 2022, Organ Procurement and Transplantation Network policy was revised to permit living kidney donation by carefully selected individuals with type 2 diabetes mellitus (T2DM), which had previously been prohibited. Early evaluation is needed to understand adoption of this practice and short-term donor outcomes. METHODS:, and proteinuria. RESULTS:, (p = 0.12), and proteinuria was present in 12% of T2DM donors vs. 7% (p = 0.23). CONCLUSIONS:Living kidney donation by individuals with T2DM is uncommon but increasing. Though differences in early outcomes for T2DM vs. non-T2DM donors were not statistically significant, these null findings are based on small sample sizes and should not be interpreted as evidence of long-term safety. Careful donor selection and longer-term follow-up are needed to better define donor risk.

INTRODUCTION/BACKGROUND:Body mass index (BMI) has long served as a screening tool for assessing procedural risk and is used as an exclusion criterion for insurance coverage. Variation in state Medicaid policies, specifically pre-transplant BMI requirements, may affect transplant eligibility and limit access to transplantation. No published literature summarizes these policies. We conducted a 50-state review of Medicaid policies to identify pre-transplant BMI requirements across solid organ transplantation (heart, lung, liver, kidney, pancreas, intestine). METHODS:Public records requests were submitted to state Medicaid offices for relevant policy documents. Policies were categorized as: (1) Restrictive: precludes transplant coverage if a specified weight/BMI cutoff is not met; (2) Somewhat restrictive: no explicit BMI requirements but deference to managed care organization (MCO)/transplant center criteria; or (3) Unrestrictive: no BMI requirements or publicly available MCO/transplant center criteria. RESULTS:Three states (HI, IA, and NC) had restrictive policies with pre-transplant weight/BMI requirements for coverage of heart, lung, and combined heart-lung transplants. None applied to kidney, liver, pancreas, or intestine transplants. Thirty-two states and DC had somewhat restrictive policies; 15 had unrestrictive policies. CONCLUSIONS:Most state Medicaid policies do not contain explicit BMI eligibility requirements for transplant coverage. Instead, BMI-based criteria typically originate from MCOs/transplant centers that establish and enforce their own criteria.

BACKGROUND:Fewer than 20% of donated lungs are transplanted. We examined the impacts of donor management and recovery at an in-hospital donor care unit (DCU) established in 2021 versus the donor hospital (DH). METHODS:(P/F ratio) were examined as a hypothesized effect modifier. We projected the national impact of improved utilization on lung transplant volume. RESULTS:After adjusting for donor differences, lung utilization was 88% higher (aRR: 1.88; 95% CI: 1.40, 2.53) in the DCU versus DH setting. Median P/F ratio increased from 275 to 348 mmHg (p<0.0001) in the DCU and explained approximately 38% of the improvement in lung utilization. Non-lung organ utilization was either improved or non-inferior in the DCU. Lung graft survival and pulmonary function were similar for recipients of DCU versus DH-managed donors. Nationally, an 88% improvement in lung utilization among donors not currently transferred to a DCU could theoretically result in ≥300 more lung transplants per year. CONCLUSIONS:Lung transplantation can be nearly doubled through lung-centric donor management in a DCU, without sacrificing recipient outcomes nor the utilization of other organs. Donor management practices to optimize lung utilization should be proliferated by establishing more DCUs and, where feasible, applying lung-centric protocols in donor hospitals.

BACKGROUND:The Increasing Organ Transplant Access (IOTA) model is a mandatory experimental payment model that aims to improve kidney transplant access using financial incentives and penalties for transplant centers randomized to model participation, based primarily on growth in transplant volume. METHODS:We used national transplant registry data to conduct a retrospective cohort study of US kidney transplant centers. We identified centers randomized to IOTA participation based on the published list of center participants. Using a difference-in-differences approach, we compared IOTA participants to non-participants on changes in overall, deceased donor, and living donor kidney transplant volume per 30 days in the period before IOTA randomization status was announced versus the 9 months following IOTA implementation. Separately among IOTA participants and among non-participants, we compared the proportional change in transplant volume when stratifying centers by pre-IOTA transplant volume, organ offer acceptance rate, and transplant rate ratio. RESULTS:Among 177 included centers, 97 (55%) were IOTA participants and 80 (45%) non-participants. Most IOTA participants (53%) and non-participants (55%) experienced a fall in overall transplant volume after model implementation. However, IOTA participants exhibited a 5% increase in transplant volume per 30 days, whereas non-participants exhibited a 3% decrease (P=0.01). The difference between groups was seen for deceased donor transplants (participants +5% vs. non-participants -9%, P<0.001), while living donor transplants rose in both groups (+7 vs. +19%, P=0.24). Among IOTA participants, there were no statistically significant differences in changes in transplant volume when comparing centers categorized by pre-IOTA transplant volume, organ offer acceptance rate, or transplant rate ratio. CONCLUSIONS:Centers randomized to IOTA participation outperformed model non-participants in changes in kidney transplant volume after model implementation, driven by an increase in deceased donor transplant volume among model participants but a fall among non-participants.

United States liver transplant policy debate has conflated two distinct mechanisms: Expedited Placement (OPTN Policy 9.10), a formal route for late turndowns when in-sequence offering risks non-utilization, and allocation out-of-sequence (AOOS), informal organ procurement organization (OPO) outreach beyond the match-run. Linking Standard Transplant Analysis and Research files to the Potential Transplant Recipient database (March 2021-December 2025), we classified 61,626 match-runs (41,638 transplanted) by pathway: in-sequence <50 offers (n=32,165), in-sequence ≥50 (n=3,450), AOOS (n=5,486), and Expedited Placement (n=537). AOOS rose 132% while Expedited Placement grew 6% and was absent in 12 of 55 OPOs. Before acceptance, a median 10 centers were contacted per AOOS graft versus 6 in-sequence, and AOOS refusals far more often covered entire waitlists (median 28 patients declined per donor versus 2). Most in-sequence acceptances occurred before donor cross-clamp (median 17.0 hours prior), whereas AOOS and Expedited Placement acceptances occurred after (0.15 and 2.42 hours). AOOS transplants occurred more frequently at centers with higher in-sequence acceptance of hard-to-place organs (Spearman 0.866), with comparable survival. These patterns are consistent with AOOS occurring after in-sequence placement has stalled, rather than as early bypass of in-sequence candidates; the circumstance Expedited Placement was designed for.

Kidneys from donation after circulatory death (DCD) donors historically had lower recovery and utilization than kidneys from donation after brain death (DBD) donors. However, the organ shortage and advances in organ preservation have increased interest in DCD transplantation. We used OPTN data to study temporal changes in U.S. DCD kidney recovery and transplantation. The percent DCD among kidney donors increased from 21.0% (Era1: 2016-2019) to 30.6% (Era2: 2020-2022) and 42.4% (Era3: 2023-2025). By 2025Q2, more than half (50.4%) of kidney donors were DCDs. Wide variability persisted across eras in percent DCD by recovering OPO (Era1 range: 0%-38%, Era2: 0%-49%, Era3: 0.4%-60%) and by transplanting center (Era1 range: 0%-48%, Era2: 0%-58%, Era3: 0%-60%). Transplanted DCD kidneys in Era3 (vs Era1 & Era2) were more likely to have donor age >60, diabetes, hypertension, obesity, and death due to cardiovascular disease or stroke; 86.8% were pumped. Though the recovered DCD kidney nonuse rate rose from 20.8% (Era1) to 29.9% (Era2) and 35.4% (Era3), nonused DCD kidney donors were age and had more comorbidities in Era 3 compared to earlier eras. Given that half of kidney donors are now DCD, refinements in DCD donor selection and management are needed to optimize recovery and utilization.

As hepatitis C virus (HCV) infection is now curable, HCV-positive donor organs have expanded the deceased donor pool. Using OPTN data, we identified adult kidney, liver, heart, and lung candidates listed between 2016-2025 and evaluated rates of consent to accept HCV antibody-positive (Ab+) and HCV viremic (NAT+) organs. Temporal trends were described, and multilevel modified Poisson regression with center-level random effects was used to estimate adjusted risk ratios for candidate factors and quantify center variation using median incidence rate ratios (MIRR). Rates of consent to accept both HCV Ab+ and NAT+ organs rose over time. By the end of follow-up, consent rates for HCV Ab+ organs were 63.1% for kidney, 80.6% for liver, 73.3% for heart, and 72.1% for lung candidates, and consent rates for NAT+ organs were 44.9%, 70.7%, 50.5%, and 46.1%, respectively. Associations between candidate characteristics and consent were small after adjustment for center, whereas center effects were large. For HCV Ab+ organs, MIRRs were 6.31 (kidney), 2.48 (liver), 2.90 (heart), and 4.36 (lung); for NAT+ organs, MIRRs were even higher, 9.65, 3.19, 4.00, and 7.30, respectively, indicating marked between-center variability. Our analyses suggest that access to HCV-positive organs is influenced more by center practices than patient characteristics.

Disparities in deceased donor liver transplantation (DDLT) may be due to geographic imbalances of organ supply-to-demand ratios and/or center organ acceptance practices. We evaluated the relative contribution of centers versus broader geographic units (DSAs/States/OPTN regions/census divisions) to variability of DDLT rate. Using SRTR data on adult, first-time, non-Status1A, period-prevalent waitlist candidates, we conducted multilevel Poisson regression with center-level and donor service area (DSA)-level intercepts to model DDLT rates across three eras: pre-acuity circles (AC)(5/1/2017-4/30/2019), initial post-AC (2/4/2020-2/3/2022), and recent post-AC (02/04/2022-12/31/2024), adjusting for candidate factors. We calculated center-level and DSA-level median incidence rate ratios (MIRRs) and their relative contributions to total geographic variation. DSA-level MIRR declined from _1.471.52_1.58 to _1.321.37_1.42 to _1.171.23_1.28 from pre-AC to initial post-AC to recent post-AC. The center-level, within-DSA MIRR increased from _1.561.61_1.64 to _1.761.82_1.84 to _1.921.96_2.05 across eras. The DSA-level contribution to total center-level variation declined from 43.7% to 21.5% to 8.4% across eras. Similarly, the contribution of other broad, geographic units (States/OPTN regions/census divisions) to total geographic variation in DDLT rates decreased after AC. Further allocation changes are unlikely to substantially reduce geographic disparities because DDLT rate variation is predominantly driven by center-level differences, which were associated with highly variable advanced preservation practices.