Faculty Bibliography

The first sustained increase in live kidney donation in the United States in 15 years was observed from 2017 to 2019. To help sustain this surge, we studied 35 900 donors (70.3% white, 14.5% Hispanic, 9.3% black, 4.4% Asian) to understand the increase in 2017-2019 vs 2014-2016 using Poisson regression. Among biologically related donors aged <35, 35-49, and ≥50 years, the number of donors did not change across race/ethnicity but increased by 38% and 29% for Hispanic and black ≥50. Among unrelated donors <35, 35-49, and ≥50, white donors increased by 18%, 14%, and 27%; Hispanic donors <35 did not change but increased by 22% and 35% for 35-49 and ≥50; black donors <35 declined by 23% and did not change for 35-49 and ≥50; Asian donors did not change. Among kidney paired donors <35, 35-49, and ≥50, white donors increased by 42%, 50%, and 68%; Hispanic donors <35 and 35-49 increased by 36% and 55% and did not change for ≥50; black donors did not change; Asian donors <35 did not change but increased by 107% and 82% for 35-49 and ≥50. The increase in donation was driven predominantly by unrelated and paired white donors. Donation among unrelated black individuals should be promoted.

BACKGROUND:Optimizing amino acid (eplet) histocompatibility at first transplant decreases the risk of de novo donor-specific antibody (dnDSA) development and may improve long-term graft survival in pediatric kidney transplant recipients (KTR). We performed a retrospective analysis of pediatric KTR and their respective donors to identify eplets most commonly associated with dnDSA formation. METHODS:Eplet mismatch analysis was performed in a cohort of 125 pediatric KTR-donor pairs (2006-2018). We determined the prevalence of each eplet mismatch and quantified the percentage of exposed patients who developed dnDSA for each mismatched eplet. RESULTS:Recipient median age was 14 (IQR 8-17) years with a racial distribution of 42% Black, 48% Caucasian, and 5.6% Middle-Eastern. Median eplet load varied significantly by recipient race, Black 82 (IQR 58-98), White 60 (IQR 44-81) and Other 66 (IQR 61-76), p = 0.002. Forty-four percent of patients developed dnDSA after median 37.1 months. Compared to dnDSA- patients, dnDSA+ patients had higher median eplet load, 64 (IQR 46-83) vs. 77 (IQR 56-98), p = 0.012. The most common target of dnDSA were eplets expressed in HLA-A*11 and A2 in Class I, and HLA-DQ6 and DQA5 in Class II. The most commonly mismatched eplets were not the most likely to result in dnDSA formation. CONCLUSIONS:In a racially diverse population, only a subset of eplets was linked to antibody formation. Eplet load alone is not a sufficient surrogate for eplet immunogenicity. These findings illustrate the need to optimize precision in donor selection and allocation to improve long-term graft outcomes. Graphical Abstract A higher resolution version of the Graphical abstract is available as Supplementary information.

BACKGROUND:Live kidney donors (LKDs) account for nearly a third of kidney transplants in the United States. While donor nephrectomy poses minimal post-surgical risk, LKDs face an elevated adjusted risk of developing chronic diseases such as hypertension, diabetes, and end-stage renal disease. Routine screening presents an opportunity for the early detection and management of chronic conditions. Transplant hospital reporting requirements mandate the submission of laboratory and clinical data at 6-months, 1-year, and 2-years after kidney donation, but less than 50% of hospitals are able to comply. Strategies to increase patient engagement in follow-up efforts while minimizing administrative burden are needed. We seek to evaluate the effectiveness of using small financial incentives to promote patient compliance with LKD follow-up. METHODS/DESIGN:We are conducting a two-arm randomized controlled trial (RCT) of patients who undergo live donor nephrectomy at The Johns Hopkins Hospital Comprehensive Transplant Center (MDJH) and the University of Maryland Medical Center Transplant Center (MDUM). Eligible donors will be recruited in-person at their first post-surgical clinic visit or over the phone. We will use block randomization to assign LKDs to the intervention ($25 gift card at each follow-up visit) or control arm (current standard of care). Follow-up compliance will be tracked over time. The primary outcome will be complete (all components addressed) and timely (60 days before or after expected visit date), submission of LKD follow-up data at required 6-month, 1-year, and 2-year time points. The secondary outcome will be transplant hospital-level compliance with federal reporting requirements at each visit. Rates will be compared between the two arms following the intention-to-treat principle. DISCUSSION:Small financial incentivization might increase patient compliance in the context of LKD follow-up, without placing undue administrative burden on transplant providers. The findings of this RCT will inform potential center- and national-level initiatives to provide all LKDs with small financial incentives to promote engagement with post-donation monitoring efforts. TRIAL REGISTRATION:ClinicalTrials.gov number: NCT03090646 Date of registration: March 2, 2017 Sponsors: Johns Hopkins University, University of Maryland Medical Center Funding: The Living Legacy Foundation of Maryland.

Many deceased-donor and living-donor kidney transplants (KTs) rely on commercial airlines for transport. However, the coronavirus-19 pandemic has drastically impacted the commercial airline industry. To understand potential pandemic-related disruptions in the transportation network of kidneys across the United States, we used national flight data to compare scheduled flights during the pandemic vs 1-year earlier, focusing on Organ Procurement Organization (OPO) pairs between which kidneys historically most likely traveled by direct flight (High Volume by direct Air transport OPO Pairs, HVA-OPs). Across the United States, there were 39% fewer flights in April 2020 vs April 2019. Specific to the kidney transportation network, there were 65.1% fewer flights between HVA-OPs, with considerable OPO-level variation (interquartile range [IQR] 54.7%-75.3%; range 0%-100%). This translated to a drop in median number of flights between HVA-OPs from 112 flights/wk in April 2019 to 34 in April 2020 (P < .001), and a rise in wait time between scheduled flights from 1.5 hours in April 2019 (IQR 0.76-3.3) to 4.9 hours in April 2020 (IQR 2.6-11.2; P < .001). Fewer flights and longer wait times can impact logistics as well as cold ischemia time; our findings motivate an exploration of creative approaches to KT transport as the impact of this pandemic on the airline industry evolves.

As the field of Vascular Composite Allotransplantation (VCA) grows, demand for VCA donations will increase. The public should be made aware of this treatment option to support patients' informed decision-making and authorization for deceased donation. We assessed the availability and quality of existing VCA public education materials from organ procurement organizations (OPOs), transplant centers, the Organ Procurement and Transplant Network, Veterans Affairs, and the Department of Defense. A content analysis was performed to identify topics covered and important gaps. In total, 1314 public education materials were analyzed, including OPO Facebook posts (61.6%), OPO Twitter posts (29.9%), websites (6.4%), and written documents (eg, fact sheets, research reports) (2.1%). Upper extremity (34.7%) and face (34.5%) transplants were more commonly covered than reproductive (6.4%) or other VCA types (2.8%). Most materials (76.6%) referenced a specific VCA story. However, few materials described which patient population could benefit from VCA (eg, Veterans, amputees, burn victims, 16.4%), the authorization requirements for VCA donation (6.6%), or the appearance of transplanted VCA organs (1.2%). Current VCA public education materials do not adequately educate the public. More comprehensive education materials are needed to prepare the public to authorize VCA donation, become potential donors, or learn about transplant options.

RATIONALE AND OBJECTIVE/OBJECTIVE:Compared to recipients of ABO-compatible (ABOc) living donor kidney transplants (LDKT), recipients of ABO-incompatible (ABOi) LDKT have a higher risk of graft loss, particularly in the first few weeks after transplantation. However, the decision to proceed with ABOi LDKT should be based on a comparison of the alternative: waiting for future ABOc LDKT (e.g, through kidney paired exchange) or for a deceased donor kidney transplant (DDKT). We sought to evaluate the patient survival difference between ABOi LDKT and waiting for an ABOc LDKT or an ABOc DDKT. STUDY DESIGN/METHODS:Retrospective cohort study of adults in the Scientific Registry of Transplant Recipients (SRTR) SETTING AND PARTICIPANTS: 808 ABOi LDKT recipients and 2423 matched controls from among 245,158 adult, first-time kidney-only waitlist registrants who did not receive an ABOi LDKT and who remained on the waitlist or received either an ABOc LDKT or an ABOc DDKT, 2002-2017 EXPOSURE: Receipt of ABOi LDKT OUTCOME: Death ANALYTICAL APPROACH: We compared mortality among ABOi LDKT recipients versus a weighted matched comparison population using Cox proportional hazards regression as well as Cox models that accommodated for changing hazards ratios over time. RESULTS:Compared to matched controls, ABOi LDKT was associated with lower survival risk in the first 30 days post-transplant (99.0% vs 99.6%, respectively), but higher survival risk beyond 180 days post-transplant. Patients who received ABOi LDKT had higher survival at 5 and 10 years (90.0% and 75.4% respectively) than similar patients who remained on the waitlist or received ABOc LDKT or ABOc DDKT (81.9% and 68.4% respectively). LIMITATIONS/CONCLUSIONS:No measurement of ABO antibody titers in recipients; eligibility of participants for kidney paired donation is unknown. CONCLUSIONS:Transplant candidates who receive an ABOi LDKT and survive more than 180 days post-transplant experience a long-term survival benefit compared to remaining on the waitlist to potentially receive an ABO compatible kidney transplant.

An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C = _0.601 0.611_0.621 ) actually outperformed GB (C = _0.581 0.591_0.601 ) and RF (C = _0.569 0.579_0.589 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.

In March 2020, coronavirus disease 2019 (COVID-19) spread rapidly nationally, causing widespread emergent changes to the health system. Our goal was to understand the impact of the epidemic on kidney transplantation (KT), at both the national and center levels, accounting statistically for waitlist composition. Using Scientific Registry of Transplant Recipients data, we compared data on observed waitlist registrations, waitlist mortality, and living-donor and deceased-donor kidney transplants (LDKT/DDKT) March 15-April 30, 2020 to expected events calculated from preepidemic data January 2016-February 2020. There were few changes before March 15, at which point the number of new listings/DDKT/LDKT dropped to 18%/24%/87% below the expected value (all P < .001). Only 12 centers performed LDKT March 15-31; by April 30, 40 centers had resumed LDKT. The decline in new listings and DDKT was greater among states with higher per capita confirmed COVID-19 cases. The number of waitlist deaths was 2.2-fold higher than expected in the 5 states with highest COVID-19 burden (P < .001). DCD DDKT and regional/national imports declined nationwide but most steeply in states with the highest COVID-19 burden. The COVID-19 epidemic has resulted in substantial changes to KT; we must adapt and learn rapidly to continue to provide safe access to transplantation and limit the growing indirect toll of an already deadly disease.

Kidney transplant recipients who develop symptoms consistent with coronavirus disease 2019 (COVID-19) are bringing unique challenges to health care professionals. Telemedicine has surged dramatically since the pandemic in effort to maintain patient care and reduce the risk of COVID-19 exposure to patients, health care workers, and the public. Herein we present reports of 3 kidney transplant recipients with COVID-19 who were managed using telemedicine via synchronous video visits integrated with an electronic medical record system, from home to inpatient settings. We demonstrate how telemedicine helped assess, diagnose, triage, and treat patients with COVID-19 while avoiding a visit to an emergency department or outpatient clinic. While there is limited information about the duration of viral shedding for immunosuppressed patients, our findings underscore the importance of using telemedicine in the follow-up care for kidney transplant recipients with COVID-19 who have recovered from symptoms but might have persistently positive nucleic acid tests. Our experience emphasizes the opportunities of telemedicine in the management of kidney transplant recipients with COVID-19 and in the maintenance of uninterrupted follow-up care for such immunosuppressed patients with prolonged viral shedding. Telemedicine may help increase access to care for kidney transplant recipients during and beyond the pandemic as it offers a prompt, safe, and convenient platform in the delivery of care for these patients. Yet, to advance the practice of telemedicine in the field of kidney transplantation, barriers to increasing the widespread implementation of telemedicine should be removed, and research studies are needed to assess the effectiveness of telemedicine in the care of kidney transplant recipients.

Clinical decision-making in kidney transplant (KT) during the coronavirus disease 2019 (COVID-19) pandemic is understandably a conundrum: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our poor understanding of these risks, many centers have paused or reduced KT activity, yet data to inform such decisions are lacking. To quantify the benefit/harm of KT in this context, we conducted a simulation study of immediate-KT vs delay-until-after-pandemic for different patient phenotypes under a variety of potential COVID-19 scenarios. A calculator was implemented (http://www.transplantmodels.com/covid_sim), and machine learning approaches were used to evaluate the important aspects of our modeling. Characteristics of the pandemic (acquisition risk, CFR) and length of delay (length of pandemic, waitlist priority when modeling deceased donor KT) had greatest influence on benefit/harm. In most scenarios of COVID-19 dynamics and patient characteristics, immediate KT provided survival benefit; KT only began showing evidence of harm in scenarios where CFRs were substantially higher for KT recipients (eg, ≥50% fatality) than for waitlist registrants. Our simulations suggest that KT could be beneficial in many centers if local resources allow, and our calculator can help identify patients who would benefit most. Furthermore, as the pandemic evolves, our calculator can update these predictions.