Faculty Bibliography

The SRTR maintains the liver-simulated allocation model (LSAM), a tool for estimating the impact of changes to liver allocation policy. Integral to LSAM is a model that predicts the decision to accept or decline a liver for transplant. LSAM implicitly assumes these decisions are made identically for adult and pediatric liver transplant (LT) candidates, which has not been previously validated. We applied LSAM's decision-making models to SRTR offer data from 2013 to 2016 to determine its efficacy for adult (≥18) and pediatric (<18) LT candidates, and pediatric subpopulations-teenagers (≥12 to <18), children (≥2 to <12), and infants (<2)-using the area under the receiver operating characteristic (ROC) curve (AUC). For nonstatus 1A candidates, all pediatric subgroups had higher rates of offer acceptance than adults. For non-1A candidates, LSAM's model performed substantially worse for pediatric candidates than adults (AUC 0.815 vs. 0.922); model performance decreased with age (AUC 0.898, 0.806, 0.783 for teenagers, children, and infants, respectively). For status 1A candidates, LSAM also performed worse for pediatric than adult candidates (AUC 0.711 vs. 0.779), especially for infants (AUC 0.618). To ensure pediatric candidates are not unpredictably or negatively impacted by allocation policy changes, we must explicitly account for pediatric-specific decision making in LSAM.

Fine particulate matter (PM_2.5 ), a common form of air pollution which can induce systemic inflammatory response, is a risk factor for adverse health outcomes. Kidney transplant (KT) recipients are likely vulnerable to PM_2.5 due to comorbidity and chronic immunosuppression. We sought to quantify the association between PM_2.5 and post-KT outcomes. For adult KT recipients (1/1/2010-12/31/2016) in the Scientific Registry of Transplant Recipients, we estimated annual zip-code level PM_2.5 concentrations at the time of KT using NASA's SEDAC Global PM_2.5 Grids. We determined the associations between PM_2.5 and delayed graft function (DGF) and 1-year acute rejection using logistic regression and death-censored graft failure (DCGF) and mortality using Cox proportional hazard models. All models were adjusted for sociodemographics, recipient, transplant, and ZIP code level confounders. Among 87 233 KT recipients, PM_2.5 was associated with increased odds of DGF (OR = 1.59; 95% CI: 1.48-1.71) and 1-year acute rejection (OR = 1.31; 95% CI: 1.17-1.46) and increased risk of all-cause mortality (HR = 1.15; 95% CI: 1.07-1.23) but not DCGF (HR = 1.05; 95% CI: 0.97-1.51). In conclusion, PM_2.5 was associated with higher odds of DGF and 1-year acute rejection and elevated risk of mortality among KT recipients. Our study highlights the importance of considering environmental exposure as risk factors for post-KT outcomes.

Recently, model for end-stage liver disease (MELD)-based liver allocation in the United States has been questioned based on concerns that waitlist mortality for a given biologic MELD (bMELD), calculated using laboratory values alone, might be higher at certain centers in certain locations across the country. Therefore, we aimed to quantify the center-level variation in bMELD-predicted mortality risk. Using Scientific Registry of Transplant Recipients (SRTR) data from January 2015 to December 2019, we modeled mortality risk in 33 260 adult, first-time waitlisted candidates from 120 centers using multilevel Poisson regression, adjusting for sex, and time-varying age and bMELD. We calculated a "MELD correction factor" using each center's random intercept and bMELD coefficient. A MELD correction factor of +1 means that center's candidates have a higher-than-average bMELD-predicted mortality risk equivalent to 1 bMELD point. We found that the "MELD correction factor" median (IQR) was 0.03 (-0.47, 0.52), indicating almost no center-level variation. The number of centers with "MELD correction factors" within ±0.5 points, and between ±0.5-± 1, ±1.0-±1.5, and ±1.5-±2.0 points was 62, 41, 13, and 4, respectively. No centers had waitlisted candidates with a higher-than-average bMELD-predicted mortality risk beyond ±2 bMELD points. Given that bMELD similarly predicts waitlist mortality at centers across the country, our results support continued MELD-based prioritization of waitlisted candidates irrespective of center.

BACKGROUND:Fine particulate matter (particulate matter with diameter <2.5 µm [PM2.5]) is associated with CKD progression and may impact the health of patients living with kidney failure. While older (aged ≥65 years) adults are most vulnerable to the impact of PM2.5, it is unclear whether older patients on dialysis are at elevated risk of mortality when exposed to fine particulate matter. METHODS:Older adults initiating dialysis (2010-2016) were identified from US Renal Data System (USRDS). PM2.5 concentrations were obtained from NASA's Socioeconomic Data and Application Center (SEDAC) Global Annual PM2.5 Grids. We investigated the association between PM2.5 and all-cause mortality using Cox proportional hazard models with linear splines [knot at the current Environmental Protection Agency (EPA) National Ambient Air Quality Standard for PM2.5 of 12 μg/m3] and robust variance. RESULTS:For older dialysis patients who resided in areas with high PM2.5, a 10 μg/m3 increase in PM2.5 was associated with 1.16-fold (95% CI: 1.08-1.25) increased risk of mortality; furthermore, those who were female (aHR = 1.26, 95% CI: 1.13-1.42), Black (aHR = 1.31, 95% CI: 1.09-1.59), or had diabetes as a primary cause of kidney failure (aHR = 1.25, 95% CI: 1.13-1.38) were most vulnerable to high PM2.5. While the mortality risk associated with PM2.5 was stronger at higher levels (aHR = 1.19, 95% CI: 1.08-1.32), at lower levels (≤12 μg/m3), PM2.5 was significantly associated with mortality risk (aHR = 1.04, 95% CI: 1.00-1.07) among patients aged ≥75 years (Pslope difference = 0.006). CONCLUSIONS:Older adults initiating dialysis who resided in ZIP codes with PM2.5 levels >12 μg/m3 are at increased risk of mortality. Those aged >75 were at elevated risk even at levels below the EPA Standard for PM2.5.

Recently, the Organ Procurement and Transplant Network approved a plan to allocate kidneys within 250-nm circles around donor hospitals. These homogeneous circles might not substantially reduce geographic differences in transplant rates because deceased donor kidney supply and demand differ across the country. Using Scientific Registry of Transplant Recipients data from 2016-2019, we used an integer program to design unique, heterogeneous circles with sizes between 100 and 500 nm that reduced supply/demand ratio variation across transplant centers. We weighted demand according to wait time because candidates who have waited longer have higher priority. We compared supply/demand ratios and average travel distance of kidneys, using heterogeneous circles and 250 and 500-nm fixed-distance homogeneous circles. We found that 40% of circles could be 250 nm or smaller, while reducing supply/demand ratio variation more than homogeneous circles. Supply/demand ratios across centers for heterogeneous circles ranged from 0.06 to 0.13 kidneys per wait-year, compared to 0.04 to 0.47 and 0.05 to 0.15 kidneys per wait-year for 250-nm and 500-nm homogeneous circles, respectively. The average travel distance for kidneys using heterogeneous, and 250-nm and 500-nm fixed-distance circles was 173 nm, 134 nm, and 269 nm, respectively. Heterogeneous circles reduce geographic disparity compared to homogeneous circles, while maintaining reasonable travel distances.

BACKGROUND:Belatacept may impair humoral immunity, impacting the effectiveness of SARS-CoV-2 mRNA vaccines in transplant recipients. We investigated immunogenicity after SARS-CoV-2 mRNA vaccines in kidney transplant recipients who are and are not taking belatacept. METHODS:Participants were recruited between December 9, 2020, and April 1, 2021. Blood samples were collected after dose 1 and dose 2 (D1, D2) and analyzed using either an anti-SARS-CoV-2 enzyme immunoassay against the S1 domain of the SARS-CoV-2 spike protein or immunoassay against the receptor-binding domain of the SARS-CoV-2 spike protein. Stabilized inverse probability of treatment weights was used to compare immunogenicity, and a weighted logistics regression was used to calculate fold change of positive response. RESULTS:Among the 609 participants studied, 24 (4%) were taking belatacept. After dose 1, 0/24 (0%) belatacept patients had detectable antibodies, compared with 77 of 568 (14%) among the equivalent nonbelatacept population (P = 0.06). After dose 2, 1/19 (5%) belatacept patients had detectable antibodies, compared with 190/381 (50%) among the equivalent nonbelatacept population (P < 0.001). Belatacept use was associated with 16.7-fold lower odds of having a positive post-D2 titer result (P < 0.01). CONCLUSIONS:Additional measures need to be explored to protect kidney transplant recipients taking belatacept. Best safety practices should be continued despite vaccination among this population.

BACKGROUND:Prescription opioid and benzodiazepine use have been associated with morbidity and mortality among some groups of solid organ transplant recipients, but implications for outcomes among lung transplant patients are not well described. METHODS:). RESULTS:). These effects were independent, and interactions were not detected. CONCLUSIONS:Benzodiazepine prescription fills before and after lung transplant, and opioid fills after transplant, are independently associated with posttransplant mortality. Review of benzodiazepine and opioid use history is relevant to risk-stratifying patients before and after lung transplant.