Faculty Bibliography

BACKGROUND:Younger kidney transplant (KT) candidates and recipients may have cognitive impairment due to chronic diseases and reliance on dialysis. METHODS:To quantify cognitive impairment burden by age across the KT care continuum, we leveraged a two-center cohort study of 3854 KT candidates at evaluation, 1114 recipients at admission, and 405 recipients at 1-year post-KT with measured global cognitive performance (3MS) or executive function (Trail Making Test). We also estimated burden of severe cognitive impairment that affects functional dependence (activities of daily living [ADL] < 6 or instrumental activities of daily living [IADL] < 8). RESULTS:Among KT candidates, global cognitive impairment (18-34 years: 11.1%; 35-49 years: 14.0%; 50-64 years: 19.5%; ≥65 years: 22.0%) and severe cognitive impairment burden (18-34 years: 1.1%; 35-49 years: 3.0%; 50-64 years: 6.2%; ≥65 years: 7.7%) increased linearly with age. Among KT recipients at admission, global cognitive impairment (18-34 years: 9.1%; 35-49 years: 6.1%; 50-64 years: 9.3%; ≥65 years: 15.7%) and severe cognitive impairment burden (18-34 years: 1.4%; 35-49 years: 1.4%; 50-64 years: 2.2%; ≥65 years: 4.6%) was lower. Despite lowest burden of cognitive impairment among KT recipients at 1-year post-KT across all ages (18-34 years: 1.7%; 35-49 years: 3.4%; 50-64 years: 4.3%; ≥65 years: 6.5%), many still exhibited severe cognitive impairment (18-34 years: .0%; 35-49 years: 1.9%; 50-64 years: 2.4%; ≥65 years: 3.5%). CONCLUSION/CONCLUSIONS:Findings were consistent for executive function impairment. While cognitive impairment increases with age, younger KT candidates have a high burden comparable to community-dwelling older adults, with some potentially suffering from severe forms. Transplant centers should consider routinely screening patients during clinical care encounters regardless of age.

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 & AIMS:Cirrhosis leads to malnutrition and muscle wasting that manifests as frailty, which may be influenced by cirrhosis aetiology. We aimed to characterize the relationship between frailty and cirrhosis aetiology. METHODS:Included were adults with cirrhosis listed for liver transplantation (LT) at 10 US centrer who underwent ambulatory testing with the Liver Frailty Index (LFI; 'frail' = LFI ≥ 4.4). We used logistic regression to associate aetiologies and frailty, and competing risk regression (LT as the competing risk) to determine associations with waitlist mortality (death/delisting for sickness). RESULTS:Of 1,623 patients, rates of frailty differed by aetiology: 22% in chronic hepatitis C, 31% in alcohol-associated liver disease (ALD), 32% in non-alcoholic fatty liver disease (NAFLD), 21% in autoimmune/cholestatic and 31% in 'other' (P < .001). In univariable logistic regression, ALD (OR 1.53, 95% CI 1.12-2.09), NAFLD (OR 1.64, 95% CI 1.18-2.29) and 'other' (OR 1.58, 95% CI 1.06-2.36) were associated with frailty. In multivariable logistic regression, only ALD (OR 1.40; 95% 1.01-1.94) and 'other' (OR 1.59; 95% 1.05-2.40) remained associated with frailty. A total of 281 (17%) patients died/were delisted for sickness. In multivariable competing risk regression, LFI was associated with waitlist mortality (sHR 1.05, 95% CI 1.03-1.06), but aetiology was not (P > .05 for each). No interaction between frailty and aetiology on the association with waitlist mortality was found (P > .05 for each interaction term). CONCLUSIONS:Frailty is more common in patients with ALD, NAFLD and 'other' aetiologies. However, frailty was associated with waitlist mortality independent of cirrhosis aetiology, supporting the applicability of frailty across all cirrhosis aetiologies.

OBJECTIVES/OBJECTIVE:The creation and maintenance of durable hemodialysis access is critically important for reducing patient morbidity and controlling overall costs within health systems. Our objective was to quantify the costs associated with hemodialysis access creation and its maintenance over time within a rate-controlled health system where charges equate to payments. METHODS:The Maryland Health Services Cost Review Commission administrative claims database was used to identify patients who underwent first-time access creation from 2012-2020. Patients were identified using CPT codes for access creation, and costs were accrued for the initial encounter and all subsequent outpatient access-related encounters. T-tests and Wilcoxon tests were used to compare reinterventions and access-related costs ($USD) between arteriovenous fistulae (AVF) and arteriovenous grafts (AVG). Multivariable modeling was used to quantify the association of access type with charge variation. RESULTS:Overall, 12,716 patients underwent first-time access creation (69.3% AVF vs. 30.7% AVG). There was no difference in freedom from reintervention between the two access types at any point following creation (HR: 1.03, 95%CI: 0.97-1.10); however, AVF were associated with a lower number of cumulative reinterventions (1.50 vs. 2.24) compared to AVG (P<0.0001). AVF was associated with lower overall costs in the year of creation ($9,388 vs. $13,539, P<0.0001), a difference that remained significant over the subsequent 3 years. The lower costs associated with AVF were present both in the costs associated with creation and subsequent maintenance. On multivariable analysis, AVF was associated with a $3,557 reduction in total access-related costs versus AVG (95%CI -$3828, -3287). CONCLUSION/CONCLUSIONS:AVF require fewer interventions and are associated with lower costs at placement and over the first three years of maintenance compared to AVG. The use of AVF for first-time hemodialysis access represents an opportunity for healthcare savings in appropriately selected patients with a high preoperative likelihood of AVF maturation.

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.