Faculty Bibliography

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.

Organ procurement organizations (OPOs) evaluate referrals for deceased organ donation in the United States. Efforts to expand the donor pool, such as the HIV organ policy equity (HOPE) Act that permits transplants from HIV-positive donors to HIV-positive recipients, can only succeed if OPOs pursue referrals. However, relatively little is known about how OPO staff evaluate referrals. To better understand this process, OPO staff completed a discrete choice experiment to quantify the relative importance of seven donor characteristics on the decision to pursue a theoretical donor. Relative importance was defined by Partworth utility using a hierarchical Bayesian conditional logit model. There were 51 respondents from 36 of 58 OPOs in the United States. Of the seven attributes, organ and tissue potential were the most influential, followed by age, type of death, HIV status, donor registration, and Hepatitis C status. To be preferred to an HIV-negative donor, an HIV-positive donor needed to have the potential to donate two additional organs. These data provide insight into the preferences of OPO referral staff and may help explain the lower than expected number of HIV-positive transplants performed since the passage of the HOPE Act.

MELD-Na appears to disadvantage women awaiting liver transplant by underestimating their mortality rate. Fixing this problem involves: (1) estimating the magnitude of this disadvantage separately for each MELD-Na, (2) designing a correction for each MELD-Na, and (3) evaluating corrections to MELD-Na using simulated allocation. Using Kaplan-Meier modeling, we calculated 90-day without-transplant survival for men and women, separately at each MELD-Na. For most scores between 15 and 35, without-transplant survival was higher for men by 0-5 percentage points. We tested two proposed corrections to MELD-Na (MELD-Na-MDRD and MELD-GRAIL-Na), and one correction we developed (MELD-Na-Shift) to target the differences we quantified in survival across the MELD-Na spectrum. In terms of without-transplant survival, MELD-Na-MDRD overcorrected sex differences while MELD-GRAIL-Na and MELD-Na-Shift eliminated them. Estimating the impact of implementing these corrections with the liver simulated allocation model, we found that MELD-Na-Shift alone eliminated sex disparity in transplant rates (p = 0.4044) and mortality rates (p = 0.7070); transplant rates and mortality rates were overcorrected by MELD-Na-MDRD (p = 0.0025, p = 0.0006) and MELD-GRAIL-Na (p = 0.0079, p = 0.0005). We designed a corrected MELD-Na that eliminates sex disparities in without-transplant survival, but allocation changes directing smaller livers to shorter candidates may also be needed to equalize women's access to liver transplant.

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.

BACKGROUND:Frailty, a phenotype characterized by decreased physiologic reserve and the inability to recover following confrontation with a stressor like hemodialysis, may help identify which patients on incident hemodialysis will experience longer postdialysis recovery times. Recovery time is associated with downstream outcomes, including quality of life and mortality. We characterized postdialysis recovery times among patients new to hemodialysis and quantified the association between frailty and hemodialysis recovery time. METHODS:Among 285 patients on hemodialysis enrolled in the Predictors of Arrhythmic and Cardiovascular Risk in End Stage Renal Disease (PACE) study, frailty was measured using the Fried phenotype. Self-reported recovery time was obtained by telephone interview. We estimated the association of frailty (intermediately frail and frail versus nonfrail) and postdialysis recovery time using adjusted negative binomial regression. RESULTS:Median time between dialysis initiation and study enrollment was 3.4 months (IQR, 2.7-4.9), and that between initiation and recovery time assessment was 11 months (IQR, 9.3-15). Mean age was 55 years, 24% were >65 years, and 73% were Black; 72% of individuals recovered in ≤1 hour, 20% recovered in 1-6 hours, 5% required 6-12 hours to recover, and <5% required >12 hours to recover. Those with intermediate frailty, frailty, and age ≤65 years had 2.56-fold (95% CI, 1.45 to 4.52), 1.72-fold (95% CI, 1.03 to 2.89), and 2.35-fold (95% CI, 1.44 to 3.85) risks, respectively, of longer recovery time independent of demographic characteristics, comorbidity, and dialysis-related factors. CONCLUSIONS:In adults new to hemodialysis, frailty was independently associated with prolonged postdialysis recovery. Future studies should assess the effect of frailty-targeted interventions on recovery time to improve clinical outcomes.

BACKGROUND:Weight loss before kidney transplant (KT) is a known risk factor for weight gain and mortality, however, while unintentional weight loss is a marker of vulnerability, intentional weight loss might improve health. We tested whether pre-KT unintentional and intentional weight loss have differing associations with post-KT weight gain, graft loss and mortality. METHODS:Among 919 KT recipients from a prospective cohort study, we used adjusted mixed-effects models to estimate post-KT BMI trajectories, and Cox models to estimate death-uncensored graft loss, death-censored graft loss and all-cause mortality by 1-year pre-KT weight change category [stable weight (change ≤ 5%), intentional weight loss (loss > 5%), unintentional weight loss (loss > 5%) and weight gain (gain > 5%)]. RESULTS:The mean age was 53 years, 38% were Black and 40% were female. In the pre-KT year, 62% of recipients had stable weight, 15% had weight gain, 14% had unintentional weight loss and 10% had intentional weight loss. In the first 3 years post-KT, BMI increases were similar among those with pre-KT weight gain and intentional weight loss and lower compared with those with unintentional weight loss {difference +0.79 kg/m2/year [95% confidence interval (CI) 0.50-1.08], P < 0.001}. Only unintentional weight loss was independently associated with higher death-uncensored graft loss [adjusted hazard ratio (aHR) 1.80 (95% CI 1.23-2.62)], death-censored graft loss [aHR 1.91 (95% CI 1.12-3.26)] and mortality [aHR 1.72 (95% CI 1.06-2.79)] relative to stable pre-KT weight. CONCLUSIONS:This study suggests that unintentional, but not intentional, pre-KT weight loss is an independent risk factor for adverse post-KT outcomes.

BACKGROUND:With stressors of dialysis prekidney transplantation (KT) and restoration of kidney function post-KT, it is likely that KT recipients experience a decline in functional status while on the waitlist and improvements post-KT. METHODS:We leveraged 224 832 KT recipients from the national registry (SRTR, February 1990-May 2019) with measured Karnofsky Performance Status (KPS, 0%-100%) at listing, KT admission, and post-KT. We quantified the change in KPS from listing to KT using generalized linear models. We described post-KT KPS trajectories using adjusted mixed-effects models and tested whether those trajectories differed by age, sex, race, and diabetes status using a Wald test among all KT recipients. We then quantified risk adverse post-KT outcomes (mortality and all-cause graft loss [ACGL]) by preoperative KPS and time-varying KPS. RESULTS:Mean KPS declined from listing (83.7%) to admission (78.9%) (mean = 4.76%, 95% confidence interval [CI]: -4.82, -4.70). After adjustment, mean KPS improved post-KT (slope = 0.89%/y, 95% CI: 0.87, 0.91); younger, female, non-Black, and diabetic recipients experienced greater post-KT improvements (Pinteractions < 0.001). Lower KPS (per 10% decrease) at admission was associated with greater mortality (adjusted hazard ratio [aHR] = 1.11, 95% CI: 1.10, 1.11) and ACGL (aHR = 1.08, 95% CI: 1.08, 1.09) risk. Lower post-KT KPS (per 10% decrease; time-varying) were more strongly associated with mortality (aHR = 1.93, 95% CI: 1.92, 1.94) and ACGL (aHR = 1.84, 95% CI: 1.83, 1.85). CONCLUSIONS:Functional status declines pre-KT and improves post-KT in the national registry. Despite post-KT improvements, poorer functional status at KT and post-KT are associated with greater mortality and ACGL risk. Because of its dynamic nature, clinicians should repeatedly screen for lower functional status pre-KT to refer vulnerable patients to prehabilitation in hopes of reducing risk of adverse post-KT outcomes.

In this study of 12 people with HIV (PWH) who received the first dose of SARS-CoV-2 mRNA vaccination, anti-SARS-CoV-2 receptor-binding domain antibodies were detectable in all participants; lower antibody levels were seen in those with lower CD4+ counts, and vaccine reactions were generally mild.