Faculty Bibliography

BACKGROUND:The Organ Procurement and Transplantation Network implemented Share 35 on June 18, 2013, to broaden deceased donor liver sharing within regional boundaries. We investigated whether increased sharing under Share 35 impacted geographic disparity in deceased donor liver transplantation (DDLT) across donation service areas (DSAs). METHODS:Using Scientific Registry of Transplant Recipients June 2009 to June 2017, we identified 86 083 adult liver transplant candidates and retrospectively estimated Model for End-Stage Liver Disease (MELD)-adjusted DDLT rates using nested multilevel Poisson regression with random intercepts for DSA and transplant program. From the variance in DDLT rates across 49 DSAs and 102 programs, we derived the DSA-level median incidence rate ratio (MIRR) of DDLT rates. MIRR is a robust metric of heterogeneity across each hierarchical level; larger MIRR indicates greater disparity. RESULTS:MIRR was 2.18 pre-Share 35 and 2.16 post-Share 35. Thus, 2 candidates with the same MELD in 2 different DSAs were expected to have a 2.2-fold difference in DDLT rate driven by geography alone. After accounting for program-level heterogeneity, MIRR was attenuated to 2.10 pre-Share 35 and 1.96 post-Share 35. For candidates with MELD 15-34, MIRR decreased from 2.51 pre- to 2.27 post-Share 35, and for candidates with MELD 35-40, MIRR increased from 1.46 pre- to 1.51 post-Share 35, independent of program-level heterogeneity in DDLT. DSA-level heterogeneity in DDLT rates was greater than program-level heterogeneity pre- and post-Share 35. CONCLUSIONS:Geographic disparity substantially impacted DDLT rates before and after Share 35, independent of program-level heterogeneity and particularly for candidates with MELD 35-40. Despite broader sharing, geography remains a major determinant of access to DDLT.

Several single-center reports of using HCV-viremic organs for HCV-uninfected (HCV-) recipients were recently published. We sought to characterize national utilization of HCV-exposed donors for HCV- recipients (HCV D+/R-) in kidney transplantation (KT) and liver transplantation (LT). Using SRTR data (April 1, 2015-December 2, 2018) and Gini coefficients, we studied center-level clustering of 1193 HCV D+/R- KTs and LTs. HCV-viremic (NAT+) D+/R- KTs increased from 1/month in 2015 to 22/month in 2018 (LTs: 0/month to 12/month). HCV-aviremic (Ab+/NAT-) D+/R- KTs increased from < 1/month in 2015 to 26/month in 2018 (LTs: <1/month to 8/month). HCV- recipients of viremic and aviremic kidneys spent a median (interquartile range [IQR]) of 0.7 (0.2-1.6) and 1.6 (0.4-3.5) years on the waitlist versus 1.8 (0.5-4.0) among HCV D-/R-. HCV- recipients of viremic and aviremic livers had median (IQR) MELD scores of 24 (21-30) and 25 (21-32) at transplantation versus 29 (23-36) among HCV D-/R-. 12 KT and 14 LT centers performed 81% and 76% of all viremic HCV D+/R- transplants; 11 KT and 13 LT centers performed 76% and 69% of all aviremic HCV D+/R- transplants. There have been marked increases in HCV D+/R- transplantation, although few centers are driving this practice; centers should continue to weigh the risks and benefits of HCV D+/R- transplantation.

Pediatric kidney transplant candidates often have multiple potential living donors (LDs); no evidence-based tool exists to compare potential LDs, or to decide between marginal LDs and deceased donor (DD) kidney transplantation (KT). We developed a pediatric living kidney donor profile index (P-LKDPI) on the same scale as the DD KDPI by using Cox regression to model the risk of all-cause graft loss as a function of living donor characteristics and DD KDPI. HLA-B mismatch (adjusted hazard ratio [aHR] per mismatch = _1.04 1.27_1.55 ), HLA-DR mismatch (aHR per mismatch = _1.02 1.23_1.49 ), ABO incompatibility (aHR = _1.20 3.26_8.81 ), donor systolic blood pressure (aHR per 10 mm Hg = _1.01 1.07_1.18 ), and donor estimated GFR (eGFR; aHR per 10 mL/min/1.73 m² = _0.88 0.94_0.99 ) were associated with graft loss after LDKT. Median (interquartile range [IQR]) P-LKDPI was -25 (-56 to 12). 68% of donors had P-LKDPI <0 (less risk than any DD kidney) and 25% of donors had P-LKDPI >14 (more risk than median DD kidney among pediatric KT recipients during the study period). Strata of LDKT recipients of kidneys with higher P-LKDPI had a higher cumulative incidence of graft loss (39% at 10 years for P-LDKPI ≥20, 28% for 20> P-LKDPI ≥-20, 23% for -20 > P-LKDPI ≥-60, 19% for P-LKDPI <-60 [log rank P < .001]). The P-LKDPI can aid in organ selection for pediatric KT recipients by allowing comparison of potential LD and DD kidneys.

The number of live kidney donors has declined since 2005. This decline parallels the evolving knowledge of risk for biologically related, black, and younger donors. To responsibly promote donation, we sought to identify declining low-risk donor subgroups that might serve as targets for future interventions. We analyzed a national registry of 77 427 donors and quantified the change in number of donors per 5-year increment from 2005 to 2017 using Poisson regression stratified by donor-recipient relationship and race/ethnicity. Among related donors aged <35, 35 to 49, and ≥50 years, white donors declined by 21%, 29%, and 3%; black donors declined by 30%, 31%, and 12%; Hispanic donors aged <35 and 35 to 49 years declined by 18% and 15%, and those aged ≥50 increased by 10%. Conversely, among unrelated donors aged <35, 35 to 49, and ≥50 years, white donors increased by 12%, 4%, and 24%; black donors aged <35 and 35 to 49 years did not change but those aged ≥50 years increased by 34%; Hispanic donors increased by 16%, 21%, and 46%. Unlike unrelated donors, related donors were less likely to donate in recent years across race/ethnicity. Although this decline might be understandable for related younger donors, it is less understandable for lower-risk related older donors (≥50 years). Biologically related older individuals are potential targets for interventions to promote donation.

The Kidney Allocation System (KAS) has resulted in fewer pediatric kidneys being allocated to pediatric deceased donor kidney transplant (pDDKT) recipients. This had prompted concerns that post-pDDKT outcomes may worsen. To study this, we used SRTR data to compare the outcomes of 953 pre-KAS pDDKT (age <18 years) recipients (December 4, 2012-December 3, 2014) with the outcomes of 934 post-KAS pDDKT recipients (December 4, 2014-December 3, 2016). We analyzed mortality and graft loss by using Cox regression, delayed graft function (DGF) by using logistic regression, and length of stay (LOS) by using negative binomial regression. Post-KAS recipients had longer pretransplant dialysis times (median 1.26 vs 1.07 years, P = .02) and were more often cPRA 100% (2.0% vs 0.1%, P = .001). Post-KAS recipients had less graft loss than pre-KAS recipients (hazard ratio [HR]: _0.35 0.54_0.83 , P = .005) but no statistically significant differences in mortality (HR: _0.29 0.72_1.83 , P = .5), DGF (odds ratio: _0.93 1.32_1.93 , P = .2), and LOS (LOS ratio: _0.96 1.06_1.19 , P = .4). After adjusting for donor-recipient characteristics, there were no statistically significant post-KAS differences in mortality (adjusted HR: _0.37 1.04_2.92 , P = .9), DGF (adjusted odds ratio: _0.94 1.41_2.13 , P = .1), or LOS (adjusted LOS ratio: _0.93 1.04_1.16 , P = .5). However, post-KAS pDDKT recipients still had less graft loss (adjusted HR: _0.38 0.59_0.91 , P = .02). KAS has had a mixed effect on short-term posttransplant outcomes for pDDKT recipients, although our results are limited by only 2 years of posttransplant follow-up.

BACKGROUND:Body mass index of living kidney donors has increased substantially. Determining candidacy for live kidney donation among obese individuals is challenging because many donation-related risks among this subgroup remain unquantified, including even basic postdonation mortality. METHODS:We used data from the Scientific Registry of Transplant Recipients linked to data from the Centers for Medicare and Medicaid Services to study long-term mortality risk associated with being obese at the time of kidney donation among 119,769 live kidney donors (1987-2013). Donors were followed for a maximum of 20 years (interquartile range 6.0-16.0). Cox proportional hazards estimated the risk of postdonation mortality by obesity status at donation. Multiple imputation accounted for missing obesity data. RESULTS:Obese (body mass index ≥ 30) living kidney donors were more likely male, African American, and had higher blood pressure. The estimated risk of mortality 20 years after donation was 304.3/10,000 for obese and 208.9/10,000 for nonobese living kidney donors. Adjusting for age, sex, race/ethnicity, blood pressure, baseline estimated glomerular filtration rate, relationship to recipient, smoking, and year of donation, obese living kidney donors had a 30% increased risk of long-term mortality compared with their nonobese counterparts (adjusted hazard ratio: 1.32, 95% CI: 1.09-1.60, P = .006). The impact of obesity on mortality risk did not differ significantly by sex, race or ethnicity, biologic relationship, baseline estimated glomerular filtration rate, or among donors who did and did not develop postdonation kidney failure. CONCLUSION:These findings may help to inform selection criteria and discussions with obese persons considering living kidney donation.

BACKGROUND:United States transplant centers are required to report follow-up data for living kidney donors for 2 years post-donation. However, living kidney donor (LKD) follow-up is often incomplete. Mobile health (mHealth) technologies could ease data collection burden but have not yet been explored in this context. METHODS:We conducted semi-structured in-depth interviews with a convenience sample of 21 transplant providers and thought leaders about challenges in LKD follow-up, and the potential role of mHealth in overcoming these challenges. RESULTS:Participants reported challenges conveying the importance of follow-up to LKDs, limited data from international/out-of-town LKDs, and inadequate staffing. They believed the 2-year requirement was insufficient, but expressed difficulty engaging LKDs for even this short time and inadequate resources for longer-term follow-up. Participants believed an mHealth system for post-donation follow-up could benefit LKDs (by simplifying communication/tasks and improving donor engagement) and transplant centers (by streamlining communication and decreasing workforce burden). Concerns included cost, learning curves, security/privacy, patient language/socioeconomic barriers, and older donor comfort with mHealth technology. CONCLUSIONS:Transplant providers felt that mHealth technology could improve LKD follow-up and help centers meet reporting thresholds. However, designing a secure, easy to use, and cost-effective system remains challenging.

Children receiving a LDLT have superior post-transplant outcomes, but this procedure is only used for 10% of transplant recipients. Better understanding about barriers toward LDLT and the sociodemographic characteristics that influence these underlying mechanisms would help to inform strategies to increase its use. We conducted an online, anonymous survey of parents/caregivers for children awaiting, or have received, a liver transplant regarding their knowledge and attitudes about LDLT. The survey was completed by 217 respondents. While 97% of respondents understood an individual could donate a portion of their liver, only 72% knew the steps in evaluation, and 69% understood the donor surgery was covered by the recipient's insurance. Individuals with public insurance were less likely than those with private insurance to know the steps for LDLT evaluation (44% vs 82%; P < 0.001). Respondents with public insurance were less likely to know someone that had been a living donor (44% vs 56%; P = 0.005) as were individuals without a college degree (64% vs 85%; P = 0.007). Nearly all respondents generally trusted their healthcare team. Among respondents, 82% believed they were well-informed about LDLT but individuals with public insurance were significantly less likely to feel well-informed (67% vs 87%; P = 0.03) and to understand how donor surgery might impact donor work/time off (44% vs 81%; P = 0.001). Substantial gaps exist in parental understanding about LDLT, including its evaluation, potential benefits, and complications. Greater emphasis on addressing these barriers, especially to individuals with fewer resources, will be helpful to expand the use of LDLT.

BACKGROUND AND OBJECTIVES:Hypertension in older kidney donor candidates is viewed as safe. However, hypertension guidelines have evolved and long-term outcomes have not been explored. We sought to quantify the 15-year risk of ESKD and mortality in older donors (≥50 years old) with versus those without hypertension. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:A United States cohort of 24,533 older donors from 1999 to 2016, including 2265 with predonation hypertension, were linked to Centers for Medicare and Medicaid Services data and the Social Security Death Master File to ascertain ESKD development and mortality. The exposure of interest was predonation hypertension. From 2004 to 2016, hypertension was defined as documented predonation use of antihypertensive therapy, regardless of systolic BP or diastolic BP; from 1999 to 2003, when there was no documentation of antihypertensive therapy, hypertension was defined as predonation systolic BP ≥140 or diastolic BP ≥90 mm Hg. RESULTS:=0.34). CONCLUSIONS:Compared with older donors without hypertension, older donors with hypertension had higher risk of ESKD, but not mortality, for 15 years postdonation. However, the absolute risk of ESKD was small.

Information about wait-list time has been reported as one of the single most frequently asked questions by individuals awaiting a transplant but data regarding wait-list time have not been processed in a useful way for pediatric candidates. To predict chance of receiving a DDLT, we identified 6471 pediatric (<18 years), non status-1A, liver-only transplant candidates between 2006 and 2017 from the SRTR. Cox regression with shared frailty for DSA level effect was used to model the association of blood type, weight, allocation PELD and MELD, and DSA with chance of DDLT. Jackknife technique was used for validation. Median (interquartile range) wait-list time was 100 (34-309) days. Non-O Blood type, higher PELD/MELD score at listing, and DSA were associated with increased chance of DDLT, while age 1-5 years and 10-18 years was associated with lower chance of DDLT (P < 0.001 for all variables). Our model accurately predicted chance of transplant (C-statistic = 0.68) and was able to predict DDLT at specific follow-up times (eg, 3 months). This model can serve as the basis for an online tool that would provide useful information for pediatric wait-list candidates.