Faculty Bibliography

BACKGROUND:Organ Procurement and Transplantation Network (OPTN) policy requires 2 years of follow-up for living kidney donors (LKDs); however, many transplant hospitals struggle to meet this requirement. We developed and tested a mobile health (mHealth) system for LKD follow-up in a pilot randomized-controlled trial (RCT). METHODS:LKDs were randomly assigned to either the intervention (mHealth + standard of care) or control arm (standard of care). We assessed OPTN policy-defined completeness and timeliness of 6-month, 1-year, and 2-year follow-ups. Four hundred LKDs were enrolled in the study (June 2018 to February 2021). RESULTS:At 6-month follow-up, a higher proportion of the intervention arm participants completed composite visits (97.5% vs. 91.5%, p = 0.01). Both arms had similar compliance rates at 1- and 2-year follow-up (92.0% vs. 89.5%, p = 0.49, and 66.5% vs. 65.0%, p = 0.83). Intervention arm participants completed 6-month follow-up 11 days earlier than their counterparts (p = 0.009). CONCLUSION/CONCLUSIONS:mHealth technologies improved 6-month follow-up, but did not impact 1- and 2-year LKD follow-up in this single-center RCT. Other strategies, such as providing services beyond data collection, may be necessary to improve donor engagement and support LDK's long-term follow-up.

INTRODUCTION/BACKGROUND:Predicting graft failure risk in pediatric liver transplantation (LT) recipients could identify areas for improving management. Persistent cognitive, motor, academic, and functional deficits are common in recipients and their impact on graft survival following LT helps inform risk prediction. METHODS:Using SRTR data 2008-2023, we evaluated the cognitive, motor, academic, and functional deficits of LT recipients at time of transplant to 14 years post-LT. We compared all cause graft failure (ACGF) among patients with versus without pre-LT and 1-year post-LT deficits using Cox regression, adjusting for recipient characteristics. We calculated an individual risk score for ACGF. RESULTS:In 8062 pediatric LT recipients median age 3 (IQR: 1, 10), 28.0%, 29.5%, 35.0%, and 79.8% of recipients had pre-LT deficits in cognition, motor, academic activity, and functional status respectively. This decreased to 23.0%, 18.1%, 14.2%, and 38.7% 1-year post-LT. Increased hazard of ACGF was noted in recipients with pre-LT decreased functional status (aHR = 1.13 (per 10% decrease), 95% CI: 1.10-1.15, p < 0.001), definite motor delay (aHR = 1.60, 95% CI: 1.21-2.10, p < 0.001), and inability to participate in academics (aHR = 1.49, 95% CI: 1.08-1.89, p = 0.01), but not delays in cognition (aHR = 0.91, 95% CI: 0.69-1.21, p = 0.19). Our risk score predicting ACGF demonstrated improved predictive performance compared to clinical parameters alone (C-statistic = 0.70 (0.67, 0.72) vs. 0.66 (0.64, 0.69), p < 0.001). CONCLUSIONS:Pediatric LT recipients with pre- or post-LT motor, academic, and functional deficits are at higher risk for ACGF. Care should be taken to assess deficits to identify patients who may benefit from functional intervention to potentially reduce ACGF risk.

BACKGROUND:Access to liver transplantation (LT) for pediatric registrants is complex and impacted by many factors. Assessing the state of pediatric LT requires understanding the balance between policy, the availability of livers, and the quantity of pediatric patients requiring LT. METHODS:Using Scientific Registry of Transplant Recipients data with Cox regression (to compare rates) and competing risk regression (to compare cumulative incidence), we evaluated pediatric patient characteristics, number of registrants transplanted, and waitlist mortality from (January 1, 2017-February 4, 2020) to (May 1, 2020-June 4, 2023) using the implementation of acuity circles to divide the eras. RESULTS:In 4314 pediatric LT registrants, transplantation rate increased in the post-policy era, compared with the pre-policy era (adjusted hazard ratio [HR], 1.05 1.12 1.20 ; P  < 0.001). When accounting for competing risks, the increase was attenuated and not statistically significant (adjusted subdistribution HR, 0.99 1.06 1.14 ; P  = 0.08); recipients were no more likely to die on the waitlist (adjusted subdistribution HR, 0.78 1.01 1.30 ; P  = 0.99). Importantly, the prevalent pediatric waitlist dropped from 396 (2017) to 225 (2023), the rate of deceased donor LT from pediatric donors increased (weighted HR, 1.20 1.31 1.42 ; P  < 0.001), and access to living donor LT increased, compared with the pre-policy era (weighted HR, 1.11 1.33 1.59 ; P  = 0.002). The transplant rate for pediatric patients did not decrease during the study period despite the introduction of acuity circles. During the study period, the prevalent waitlist shrank, access to LT from pediatric donors increased, and access to living donor LT increased. CONCLUSIONS:Comprehensive assessment following the policy change is necessary to ensure that pediatric candidates maintain priority. Changes in pediatric transplantation are modest and likely related to changes in the pool, rather than to the policy of acuity circles.

Liver transplant (LT) recipients experience a wide range of comorbidities, leading to frequent healthcare encounters. Until now, national registries, which have limited exposures and outcomes, and laborious small cohort studies have been the main data sources for LT research. Cosmos database offers electronic health record (EHR)-based insights into LT recipients at the national level with granular data. We evaluated if Cosmos data is representative of the entire US LT recipient population. Using Cosmos (N=20,235) and the national Scientific Registry of Transplant Recipients (SRTR) (N=51,281), we identified adult, first-time LT recipients between 7/2016-12/2022. We compared demographics, clinical data, and mortality across datasets, calculating Kaplan-Meier survival estimates and multi-variable Cox regressions. Recipient characteristics were highly comparable (e.g., female: Cosmos=36.5% vs. SRTR=36.4%, Black: 6.8% vs. 7.2%; BMI: 28.5 kg/m2 [24.8-32.9] vs. 28.2 [24.6-32.4]). Lab values were similar across cohorts, including MELD (24 [17-30] vs. 23 [16-30]). Transplant indications, donor characteristics, and 5-year survival (Cosmos 83.1% [82.3-83.8) vs. SRTR 80.9% [80.4-81.3]) were similar. The associations of clinical factors with survival were similar across both groups. Cosmos database demonstrated acceptable generalizability to the general US LT recipient population, which may advance LT research through a better understanding about LT recipients' experiences and outcomes.

Since their early development in the 1980s, Simulated Allocation Models (SAMs) have helped policymakers forecast the impact of proposed allocation policy changes on patient outcomes before implementation. In the United States, models like the Kidney-Pancreas Simulated Allocation Model, Liver Simulated Allocation Model, and Thoracic Simulated Allocation Model have been instrumental in shaping organ allocation policies. Analogous models have emerged globally, including the ETKidney and Eurotransplant Liver Allocation System simulators for the Eurotransplant region, to address country and region-specific allocation challenges. This review categorizes and compares SAMs based on their core assumptions, data, and modeling approaches. We highlight challenges in model validation, the use of synthetic data, and model transparency. While simplifying assumptions are often necessary because of limited data, their influence on results should be clearly communicated to ensure policymakers can interpret model predictions accurately. Furthermore, model validation using both retrospective and prospective data is essential to assess performance under evolving policies. Greater transparency through open-source models, detailed reporting of assumptions, and validation efforts can enhance collaboration, reproducibility, and confidence in transplant research. By providing a global perspective on SAMs, this review aims to inform future research and policy development, promoting evidence-based policy development in organ transplantation.

BACKGROUND/UNASSIGNED:Understanding center-level decision-making for suboptimal kidney (SOK) offers is critical to ensure utilization of all transplantable kidneys. METHODS/UNASSIGNED:We quantified center-level variation in accepting SOK deceased donor kidney transplant (DDKT) offers using 2021-2023 national registry data. SOK subtypes included: donor age >60, ultimate cold ischemia time >24 h, hepatitis C positive, terminal serum creatinine >2.0 mg/dL, donation after circulatory death, kidney donor profile index >85%, and public health service increased risk donors. Gini coefficient (Gini) was used to analyze inequality in DDKT utilization by SOK subtype. Multilevel logistic regression models were used to calculate the median odds ratio (mOR), measuring center-level variation in accepting SOK donor offers among adult centers. RESULTS/UNASSIGNED:Of all DDKTs, 72.6% were from donors with at least 1 SOK characteristic. Inequality persisted in utilization of SOK DDKTs (Gini of all SOKs: 0.53, Gini of all non-SOKs: 0.47). The 193 adult centers accepted a median (interquartile range) of 12.5% (8.4%-19.2%) offered non-SOK donors and 7.2% (4.6%-10.8%) offered SOK donors. Non-SOK donors and SOK donors were refused by a median (interquartile range) of 5 (3-10) and 9 (4-23) centers, respectively. The SOK subtypes with the least and the most center-level variance in acceptance were increased risk donor (mOR = 2.06) and cold ischemia time >36 h (mOR = 4.86), respectively. CONCLUSIONS/UNASSIGNED:Centers vary sharply in their willingness to accept certain types of SOK offers. Informing centers of their patterns of accepting specific donor phenotypes compared with their peers may motivate centers to accept more SOKs for clinically suitable recipients, thus improving patient access to DDKT.

BACKGROUND/UNASSIGNED:Kidney transplant recipients are at risk for adverse health outcomes. Digital health tools such as wearable accelerometers and continuous glucose monitors (CGMs) can provide detailed, noninvasive tracking of health behaviors and measures, such as physical activity, sleep, and glucose levels, that may offer insights into future health concerns, such as posttransplant diabetes mellitus, cognitive health, and transplant rejection. However, there is limited evidence on the feasibility and acceptability of these devices in kidney transplant candidates older than 50 y. METHODS/UNASSIGNED:This observational cross-sectional pilot study aimed to examine the feasibility of 2 digital health tools: an accelerometer and a continuous glucose monitor. Participants were eligible for the study if they were living donor kidney transplant candidates, aged 50 y or older, had no known cognitive impairments, and could provide informed consent. Participants were asked to wear a CGM and an accelerometer for up to 14 d before their kidney transplant surgery. Device feasibility was quantified by (1) the total time the devices were worn, and (2) the validated System Usability Scale survey administered after the devices were returned. RESULTS/UNASSIGNED:20 participants enrolled in the study (mean age 64 ± 9 y, 25% women, 40% with type 2 diabetes). The median number of days of accelerometer and CGM wear were 7 (interquartile range, 6-10) d and 7 (interquartile range, 7-10) d, respectively. Ninety percent of participants reported a favorable opinion of both devices. Participants wore the CGM 100% of the time and the accelerometer 90% of the time, indicating high adherence. CONCLUSIONS/UNASSIGNED:The use of digital devices was acceptable among kidney transplant candidates aged older than 50 y, paving the way for larger studies to identify early digital biomarkers of health outcomes in this high-risk population.

INTRODUCTION/BACKGROUND:Following implementation of the U.S. Kidney Allocation System (KAS) in 2014, deceased donor kidneys with a kidney donor profile index (KDPI) < 35% are prioritized for allocation to pediatric candidates. Early post-KAS data suggested this prioritization may have led to more frequent delayed graft function compared to pre-KAS, when pediatric allocation priority was based on donor age < 35 years. We sought to understand the impact of this allocation change on longer-term pediatric kidney transplant outcomes. METHODS:We used SRTR data to identify all deceased donor kidney transplants with pediatric recipients during two eras: "Pre-KAS" (12/1/2009-11/30/2014) and "KAS" (12/1/2015-11/30/2020). We used Cox proportional hazards models to calculate the association between study era and all-cause graft failure (graft failure or death) after adjusting for recipient characteristics. RESULTS:, p = 0.001). Results were similar in sensitivity analyses limited to recipients < 10 years old and recipients alive with a functioning graft 90 days post-transplant. CONCLUSIONS:KDPI-based prioritization of kidneys for pediatric allocation was associated with a lower risk of graft failure compared to donor age-based prioritization. Further refining donor risk scores may enable additional improvements in graft survival.

BACKGROUND:Kidney transplantation (KT) from donors with human immunodeficiency virus (HIV-1) to recipients with HIV (HIV D+/R+) is noninferior to KT from donors without HIV (HIV D-/R+) with regard to safety. However, there may be differences in posttransplant infections. METHODS:We performed a secondary analysis of the HOPE in Action KT Study (NCT02602262) comparing the time to first clinically relevant infection within 24 months posttransplantation in 99 HIV D+/R+ versus 99 HIV D-/R+. Secondary outcomes included incidence rates, infection-related death, and timing of clinically relevant infection, each stratified by donor HIV status. RESULTS:The cumulative incidence of a clinically relevant infection at 24 months posttransplantation was 73.8% (95% confidence interval [CI]: 63.1%-81.2%) for HIV D+/R+ versus 64.7% (95% CI: 53.0%-73.4%) for HIV D-/R+. Comparing time to first clinically relevant infection in HIV D+/R+ versus HIV D-/R+, the adjusted hazard ratio (aHR) was 1.44 (95% CI: 1.01-2.04) at 24 months posttransplantation; for infections associated with hospitalization, the aHR was not significantly higher (1.21 [95% CI: .78-1.86). There were no significant differences in the number of infections, death from infection, duration, or site of infection between HIV D+/R+ versus HIV D-/R+, though viral infections were numerically more common in HIV D+/R+ (40% vs 35%). CONCLUSIONS:Although there was a statistically significant association between receipt of a kidney from a donor with HIV and time to first clinically relevant infection in the 24 months posttransplantation, there were no differences in infections associated with hospitalization. These data are overall reassuring as this emerging practice expands into clinical care. Clinical Trials Registration. NCT02602262.