Faculty Bibliography

SIGNIFICANCE STATEMENT:Cardiovascular diseases account for 32% of deaths among kidney transplant recipients. Statin therapy is common in this population. However, its effect on mortality prevention remains unclear among kidney transplant recipients, whose clinical risk profile might be unique because of concomitant immunosuppressive therapy. In this national study of 58,264 single-kidney transplant recipients, statin use was associated with a 5% decrease in mortality. More importantly, this protective association was stronger among those who used a mammalian target of rapamycin (mTOR) inhibitor for immunosuppression (27% decrease in mTOR inhibitor users versus 5% in nonusers). Our results suggest that statin therapy may reduce mortality in kidney transplant recipients and that the strength of this protective association may vary by immunosuppression regimen. BACKGROUND:Cardiovascular diseases are the leading cause of mortality in kidney transplant (KT) recipients, accounting for 32% of deaths. Statins are widely used in KT recipients, but effectiveness for preventing mortality remains unclear in this population, especially because of interaction between statins and immunosuppressive agents. We analyzed a national cohort to assess the real-world effectiveness of statins for reducing all-cause mortality in KT recipients. METHODS:We studied statin use and mortality among 58,264 adults (18 years or older) who received single kidneys between 2006 and 2016 and had Medicare part A/B/D. Statin use was ascertained from Medicare prescription drug claims and deaths from Center for Medicare and Medicaid Services records. We estimated the association of statin use with mortality using multivariable Cox models, with statin use as a time-varying exposure and immunosuppression regimen as effect modifiers. RESULTS:Statin use increased from 45.5% at KT to 58.2% at 1-year post-KT to 70.9% at 5-year post-KT. We observed 9785 deaths over 236,944 person-years. Overall, statin use was significantly associated with lower mortality (adjusted hazard ratio [aHR], 0.95; 95% confidence interval [CI], 0.90 to 0.99). The strength of this protective association varied by calcineurin inhibitor use (among tacrolimus users, aHR, 0.97; 95% CI, 0.92 to 1.03 versus among calcineurin nonusers, aHR, 0.72; 95% CI, 0.60 to 0.87; interaction P =0.002), mammalian target of rapamycin (mTOR) inhibitor use (among mTOR inhibitor users, aHR, 0.73; 95% CI, 0.57 to 0.92 versus among nonusers, aHR, 0.95; 95% CI, 0.91 to 1.00; interaction P =0.03), and mycophenolate use (among mycophenolate users, aHR, 0.96; 95% CI, 0.91 to 1.02 versus among nonusers, aHR, 0.76; 95% CI, 0.64 to 0.89; interaction P =0.002). CONCLUSION:Real-world evidence supports statin therapy for reducing all-cause mortality in KT recipients. Effectiveness might be greater when combined with mTOR inhibitor-based immunosuppression.

Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.

OBJECTIVE:To examine whether body mass index (BMI) changes modify the association between kidney donation and incident hypertension. BACKGROUND:Obesity increases hypertension risk in both general and living kidney donor (LKD) populations. Donation-attributable risk in the context of obesity, and whether weight change modifies that risk, is unknown. METHODS:Nested case-control study among 1558 adult LKDs (1976-2020) with obesity (median follow-up: 3.6 years; interquartile range: 2.0-9.4) and 3783 adults with obesity in the Coronary Artery Risk Development in Young Adults (CARDIA) and Atherosclerosis Risk in Communities (ARIC) studies (9.2 y; interquartile range: 5.3-15.8). Hypertension incidence was compared by donor status using conditional logistic regression, with BMI change investigated for effect modification. RESULTS:Overall, LKDs and nondonors had similar hypertension incidence [incidence rate ratio (IRR): 1.16, 95% confidence interval (95% CI): 0.94-1.43, P =0.16], even after adjusting for BMI change (IRR: 1.25, 95% CI: 0.99-1.58, P =0.05). Although LKDs and nondonors who lost >5% BMI had comparable hypertension incidence (IRR: 0.78, 95% CI: 0.46-1.34, P =0.36), there was a significant interaction between donor and >5% BMI gain (multiplicative interaction IRR: 1.62, 95% CI: 1.15-2.29, P =0.006; relative excess risk due to interaction: 0.90, 95% CI: 0.24-1.56, P =0.007), such that LKDs who gained weight had higher hypertension incidence than similar nondonors (IRR: 1.83, 95% CI: 1.32-2.53, P <0.001). CONCLUSIONS:Overall, LKDs and nondonors with obesity had similar hypertension incidence. Weight stability and loss were associated with similar hypertension incidence by donor status. However, LKDs who gained >5% saw increased hypertension incidence versus similar nondonors, providing support for counseling potential LKDs with obesity on weight management postdonation.

Background: Pediatric transplant candidates have historically been disadvantaged on the transplant waitlist, with nearly half of pediatric deceased donor organs allocated to adult recipients (Hsu, Gastroenterology, 2017), and allocation pediatric end-stage liver disease (PELD) scores that underestimate children's expected 3-month mortality compared to that of adult patients (Chang, JAMA Pediatrics, 2018). Disparities in organ distribution prompted revision of the liver allocation policy in 2020 from donation services areas (DSA) to a series of distance-based concentric circles called acuity circles (AC) before being offered nationally (US GAO, 2022), which was designed to minimize geographic inequity in liver transplant. Prior to implementation of the new liver allocation policy, analysis using the Liver Simulated Allocation Model projected that AC allocation would decrease disparities for pediatric liver transplant candidates and recipients by increasing transplants and decreasing waitlist mortality (Mogul, Transplantation, 2020). In this study, we evaluate differences in pediatric whole liver transplants performed before and after the implementation of acuity circle liver allocation policy.
Study Design: We evaluated patient characteristics, adjusted MELD/PELD at time of transplant, calculated donor age at time of transplant among pediatric whole liver transplant recipients in low versus high-volume pediatric liver transplant centers performed before and after implementation of AC-based liver allocation policy using the Scientific Registry of Transplant Recipients.
Result(s): Before and after the implementation of ACs, differences in pediatric liver transplants by age group (<2 years, 2-5 years old, 5-12 years old, and 12-18 years old) remained significantly different between low and high-volume pediatric transplant centers. Under DSA allocation policy, the median MELD/PELD at transplant was 37.0 (IQR 30.0-41.0) in low-volume centers and 40.0 (IQR 30.0-41.0) in high-volume centers. After the implementation of acuity circles, median MELD/PELD at transplant decreased to 35.0 (IQR 21.0-41.0) in low-volume centers and 35.0 (IQR 25.0-41.0) in high-volume centers. Finally, donor age at time of transplant increased from 8.0 (IQR 2.00-18.0) to 13.5 (IQR 4.5-21.0) at low-volume centers, and from 3.0 (IQR 1.0-14.0) to 4.0 (IQR 1.0-14.0) at high-volume centers before and after the implementation of ACs.
Conclusion(s): The change from DSAs to ACs in allocation policy and the shift from regional to national review boards have affected the characteristics of organ recipients, adjusted MELD/PELD at time of transplant, and donor age at time of transplant differentially between whole liver transplant recipients at low-and high-volume pediatric liver transplant centers

Background. Coronavirus disease 2019 (COVID-19)"“associated pulmonary aspergillosis (CAPA) is likely underdiagnosed, and current diagnostic tools are either invasive or insensitive. Methods. A retrospective study of mechanically ventilated patients with COVID-19 admitted to 5 Johns Hopkins hospitals between March 2020 and June 2021 was performed. Multivariable logistic regression was used for the CAPA prediction model building. Performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). Results. In the cohort of 832 patients, 98 (11.8%) met criteria for CAPA. Age, time since intubation, dexamethasone for COVID-19 treatment, underlying pulmonary circulatory diseases, human immunodeficiency virus, multiple myeloma, cancer, or hematologic malignancies were statistically significantly associated with CAPA and were included in the CAPA prediction model, which showed an AUC of 0.75 (95% confidence interval, .70"“.80). At a screening cutoff of ≥0.085, it had a sensitivity of 82%, a specificity of 51%, a positive predictive value of 18.6%, and a negative predictive value of 95.3%. (The CAPA screening score calculator is available at www.transplantmodels.com). Conclusions. We developed a CAPA risk score as a noninvasive tool to aid in CAPA screening for patients with severe COVID-19. Our score will also identify a group of patients who are unlikely to have CAPA and who therefore need not undergo additional diagnostics and/or empiric antifungal therapy.

BACKGROUND:The Organ Procurement and Transplantation Network (OPTN) is eliminating geographic boundaries in liver allocation, in favor of continuous distribution. Continuous distribution allocates organs via a composite allocation score (CAS): a weighted sum of attributes like medical urgency, candidate biology, and placement efficiency. The opportunity this change represents, to include new variables and features for prioritizing candidates, will require lengthy and contentious discussions to establish community consensus. Continuous distribution could instead be implemented rapidly by computationally translating the allocation priorities for pediatric, status 1, and O/B blood type liver candidates that are presently implemented via geographic boundaries into points and weights in a CAS. METHODS:Using simulation with optimization, we designed a CAS that is minimally disruptive to existing prioritizations, and that eliminates geographic boundaries and minimizes waitlist deaths without harming vulnerable populations. RESULTS:Compared with Acuity Circles (AC) in a 3-year simulation, our optimized CAS decreased deaths from 7771.2 to 7678.8 while decreasing average (272.66 NM vs. 264.30 NM) and median (201.14 NM vs. 186.49 NM) travel distances. Our CAS increased travel only for high MELD and status 1 candidates (423.24 NM vs. 298.74 NM), and reduced travel for other candidates (198.98 NM vs. 250.09 NM); overall travel burden decreased. CONCLUSION/CONCLUSIONS:Our CAS reduced waitlist deaths by sending livers for high-MELD and status 1 candidates farther, while keeping livers for lower MELD candidates nearby. This advanced computational method can be applied again after wider discussions of adding new priorities conclude; our method designs score weightings to achieve any specified feasible allocation outcomes.

UNLABELLED:Following the outbreak of coronavirus disease 2019 (COVID-19) in the United States, the number of kidney waitlist additions and living-donor and deceased-donor kidney transplants (LDKT/DDKT) decreased substantially but began recovering within a few months. Since then, there have been several additional waves of infection, most notably, the Delta and Omicron surges beginning in August and December 2021, respectively. METHODS/UNASSIGNED:Using SRTR data, we compared observed waitlist registrations, waitlist mortality, waitlist removal due to deteriorating condition, LDKT, and DDKT over 5 distinct pandemic periods to expected events based on calculations from preepidemic data while accounting for seasonality and secular trends. RESULTS/UNASSIGNED:). CONCLUSIONS/UNASSIGNED:Despite exceptionally high COVID-19 incidence during the Omicron wave, the transplant system responded similarly to prior waves that imposed a lesser disease burden, demonstrating the transplant system's growing adaptations and resilience to this now endemic disease.

BACKGROUND:Medication non-adherence contributes to post-transplant graft rejection and failure; however, limited knowledge about the reasons for non-adherence hinders the development of interventions to improve adherence. We conducted focus groups with solid organ transplant recipients regarding overlooked challenges in the process of transplant medication self-management and examined their adherence strategies and perceptions towards the post-transplant medication regimen. METHODS:We conducted four focus groups with n = 31 total adult transplant recipients. Participants had received kidney, liver, or combined liver/kidney transplant at Johns Hopkins Hospital between 2014 and 2019. Focus groups were audio-recorded and transcribed. Transcripts were analyzed inductively, using the constant comparative method. RESULTS:Responses generally fell into two major categories: (1) barriers to adherence and (2) "adherence landscape". We define the former as factors directly labeled as barriers to adherence by participants and the latter as factors that heavily influence the post-transplant medication self-management process. CONCLUSIONS:We propose a shift in the way healthcare providers and researchers, address the question of medication non-adherence. Rather than asking why patients are non-adherent, we suggest that constructing and understanding patients' "adherence landscape" will provide an optimal way to align the goals of patients and providers and boost health outcomes.

Kidney transplant recipients (KTRs) show poorer response to SARS-CoV-2 mRNA vaccination, yet response patterns and mechanistic drivers following third doses are ill-defined. We administered third monovalent mRNA vaccines to n = 81 KTRs with negative or low-titer anti-receptor binding domain (RBD) antibody (n = 39 anti-RBD^NEG; n = 42 anti-RBD^LO), compared with healthy controls (HCs, n = 19), measuring anti-RBD, Omicron neutralization, spike-specific CD8⁺%, and SARS-CoV-2-reactive T cell receptor (TCR) repertoires. By day 30, 44% anti-RBD^NEG remained seronegative; 5% KTRs developed BA.5 neutralization (vs 68% HCs, P < .001). Day 30 spike-specific CD8⁺% was negative in 91% KTRs (vs 20% HCs; P = .07), without correlation to anti-RBD (r_s = 0.17). Day 30 SARS-CoV-2-reactive TCR repertoires were detected in 52% KTRs vs 74% HCs (P = .11). Spike-specific CD4⁺ TCR expansion was similar between KTRs and HCs, yet KTR CD8⁺ TCR depth was 7.6-fold lower (P = .001). Global negative response was seen in 7% KTRs, associated with high-dose MMF (P = .037); 44% showed global positive response. Of the KTRs, 16% experienced breakthrough infections, with 2 hospitalizations; prebreakthrough variant neutralization was poor. Absent neutralizing and CD8⁺ responses in KTRs indicate vulnerability to COVID-19 despite 3-dose mRNA vaccination. Lack of neutralization despite CD4⁺ expansion suggests B cell dysfunction and/or ineffective T cell help. Development of more effective KTR vaccine strategies is critical. (NCT04969263).