Faculty Bibliography

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.

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.

BACKGROUND:As a 6-month waiting period is required to receive exception points to prioritize patients with hepatocellular carcinoma (HCC) for liver transplantation, prompt addition to the waitlist is critical in access to LT. METHODS:Using Epic Cosmos data on patients with HCC referred for LT 1/2018-10/2024, we used modified Poisson regression to calculate rates of waitlisting. Center-level and individual (socioeconomic, geographic, and insurance) factors were measured among those who progressed. RESULTS:Among 11,422 HCC patients referred for LT at 70 centers, with median age 63 [IQR: 58, 68], 71.5% initiated evaluation and, of those who began evaluation, 57.6% were waitlisted for LT. Of those referred, patients who were older (age 70+ vs. 51-60; RR 0.77, 95% CI: 0.65-0.90, p < 0.001), on Medicaid (0.83, 95% CI: 0.71-0.97, p = 0.02), never-married (0.82, 95% CI: 0.73-0.91, p < 0.001), or low SES (Q4: 0.87, 95% CI: 0.77-0.97, p = 0.002) had lower rates of waitlisting. Among waitlisted patients, median time from referral was 3.3 months [IQR: 2.0, 5.3]. Despite adjustment for patient level covariates, there was high center-level variation in rate of waitlisting within 12 months; 13% of centers listed patients at a rate ≥ 20% below the national median. CONCLUSION/CONCLUSIONS:Only a fraction of referred patients with HCC are waitlisted for LT. High variation in access to waitlisting based on non-clinical factors suggests barriers to waitlisting that must be addressed. Centers should focus on interventions to reduce barriers to waitlisting in patients with HCC.

KEY POINTS/CONCLUSIONS:In this cohort study of 720,348 adults referred for kidney transplantation from 2014 to 2025, only 48% were evaluated and 19% were waitlisted. Progression from referral to evaluation, waitlisting and kidney transplantation was limited by individual, center-level, and geographic factors. Some centers evaluated and waitlisted patients at rates far below the national average, and low-volume centers had lower rates of transplantation. BACKGROUND:Kidney transplantation is a cost-effective, lifesaving treatment of kidney failure, compared with dialysis. Unfortunately, most patients with kidney failure never undergo transplantation. METHODS:Using Epic Cosmos electronic health record data on all patients referred for kidney transplantation from 2014 to 2025, we assessed the stage-specific progression and attrition in the process of evaluation, waitlisting, and kidney transplantation. Center-level and individual (socioeconomic, geographic, and insurance status) factors associated with access to evaluation, waitlisting, and kidney transplantation were characterized using modified Poisson regression. RESULTS:Among 720,348 referred candidates, the median age was 55 years (interquartile range [IQR], 42-64); 47% of patients were White, 52% were male, and 87% were English speaking. Eighty-five percent of patients lived in urban areas. Of the referred candidates, 48% initiated evaluation, 19% were waitlisted, and 10% ultimately underwent transplantation. Among the referred patients who initiated evaluation, the median (IQR) time to evaluation initiation was two (1-4) months after referral; among the patients who were waitlisted, the median (IQR) time to waitlisting was four (2-9) months after evaluation initiation. Patients who were never married (0.94; 95% confidence interval [CI], 0.93 to 0.94), had severe obesity (0.70; 95% CI, 0.69 to 0.72), or were from rural zip codes (relative risk, 0.98; 95% CI, 0.97 to 1.00) were less likely to initiate evaluation. Low-volume centers had lower relative rates of transplantation (0.92; 95% CI, 0.88 to 0.96). In centers with documentation for nonprogression to evaluation, reasons for removal included not meeting criteria/not a candidate (18%), patient decision (13%), unable to contact (12%), death (4%), and financial/insurance complications (7%). CONCLUSIONS:Our study shows substantial attrition before kidney transplant waitlisting.

BACKGROUND:Robotic pancreaticoduodenectomy (RPD) is increasingly performed in the United States. Understanding factors associated with safe adoption of RPD is critical to reducing perioperative mortality during the learning curve. METHODS:Using the Epic Cosmos database, the study identified adult patients (age ≥18 years) who underwent pancreaticoduodenectomy (PD) between 2019 and 2025. Modified Poisson regression was used to assess factors associated with 30-day mortality using adjustment for age, sex, race, ethnicity, insurance, marital status, rural/urban residence, socioeconomic status, and diagnosis. Among surgeons performing two or more RPDs, mortality trends were analyzed across case-number thresholds. Mortality risk was assessed by cumulative RPD and open PD (OPD) experience, with adjustment for age and diagnosis. RESULTS:Among 23,995 patients with a median age of 69 years (interquartile range [IQR], 62-75 years), 1578 (6.6 %) underwent RPD. Use of RPD increased from 4% of PD in 2019 to 10% in 2025. The 30-day mortality was higher for RPD than for OPD (2.7 % vs 2.0 %; adjusted relative risks [aRR], 1.43 (IQR, 1.02-1.95; p = 0.029). In RPD, mortality decreased with increasing surgeon prior experience: 3.9 % (Q1: 0-1 cases), 3.9 % (Q2: 2-4 cases), 2.22 % (Q3: 5-8 cases), 2.67 % (Q4: 9-18 cases), 0.92 % (Q5: 19-71 cases). Increased RPD experience was associated with decreased mortality (per doubling RPD experience: aRR, 0.78 (95 % confidence interval [CI], 0.63-0.96; p = 0.02). The patients who underwent PD between 2023 and 2025 showed no adjusted increase in mortality with robotic technique (aRR, 1.04; 95 % CI, 0.61-1.65; p = 0.85). CONCLUSIONS:Nationwide, adoption of RPD is associated with increased 30-day mortality, which decreases substantially with increasing surgeon RPD experience. These findings suggest that structured, competency-based training pathways are needed to ensure safe dissemination of novel technology, including RPD.

BACKGROUND:Before KAS250 (circles-based allocation), donor service area (DSA) of listing was the largest contributor to deceased donor kidney transplantation (DDKT) rate disparities. Both before and after KAS250, it is unclear to what extent DSA-level disparities are attributable to center-level practice variation. We aimed to disentangle contributions to DDKT rate variation from: (1) center practices, (2) kidney distribution within sharp policy boundaries (DSAs, OPTN Regions), and (3) other geographic variation in kidney scarcity. METHODS:With national transplant registry data, we studied transplant rate variation in the pre-KAS250 era, which prioritized patients based on DSAs and Regions, and under KAS250, which prioritizes patients within 250 nautical mile circles. We modeled candidate DDKT rates with multilevel Poisson regression, adjusting for candidate factors, and calculated median incidence rate ratios (MIRR) to summarize variation attributable to DSAs, OPTN regions, states, census divisions, and to centers within those units. RESULTS:). Adjusted center-level DDKT rates under KAS250 were highly associated with offer acceptance rates (ρ = 0.60, p < 0.001). CONCLUSIONS:Though geographic disparities are driven primarily by center-level practice differences including offer acceptance, KAS250 did reduce DSA-level disparities. Further allocation policy changes are unlikely to substantially reduce geographic variation in DDKT rates.

INTRODUCTION/BACKGROUND:In Mexico, more than 15,000 patients are waiting for a kidney transplant, and there are not enough kidneys from deceased donors to transplant them. A kidney exchange program could increase kidney transplants from living donors by matching altruistic living donors and biologically incompatible donor-recipient pairs. Several countries have implemented successful kidney exchange programs. We evaluated the impact of implementing a kidney exchange program in Mexico. METHODS:We simulated kidney exchange in Mexico using data from Mexican population distributions. We used an optimization model to maximize the number of compatible patient-donor matchings. Three different scenarios were evaluated. RESULTS:We estimated that almost 45% of patients on the waiting list have an incompatible donor, and 995 transplant candidates who have a living donor available are added to the waiting list annually. If a kidney exchange program were established in Mexico, the number of living-donor transplants could increase by up to 20%. CONCLUSIONS:Implementing kidney exchange in the country may reduce the increase in the number of recipients on the waiting list and reduce costs in the long term. To succeed, the program must not only draw sufficient participation from incompatible pairs, but also ensure that these pairs remain in the program even if they have to wait to be matched.