Faculty Bibliography

BACKGROUND:Short and long sleep durations are associated with cognitive dysfunction. Given the increased prevalence of sleep abnormalities in the CKD population, we tested whether the association between sleep duration and cognitive function differed between older adults with and without CKD. METHODS:This was a study of 3215 older adults (aged ≥ 60years) enrolled in the National Health and Nutrition Examination Survey (2011-2014) evaluating sleep duration, cognitive function (immediate recall, delayed recall, verbal fluency, executive function and processing speed, and global cognition), and kidney function. We quantified the association between sleep duration and cognitive function using linear regression and tested whether the associations differed among those with CKD and without using a Wald test for interaction. RESULTS:Among 3215 participants, 13.3% reported 2-5 hours of sleep per day, 75.2% reported 6-8 hours, and 11.5% reported ≥ 9 hours. Persons with CKD were more likely to sleep ≥ 9 hours (OR = 1.73, 95% CI: 1.22-2.46). Among participants with CKD, those with sleep duration ≥ 9 hours demonstrated worse global cognitive function (p for interaction = 0.01), immediate recall (p for interaction = 0.01), and verbal fluency (p for interaction = 0.004) than those with 6-8 hours sleep duration; no differences were observed for participants with CKD who slept 2-5 hours. Among participants without CKD, sleep was not associated with any measures of cognitive function. CONCLUSIONS:Longer sleep duration is associated with worse cognitive function only among persons with CKD and global cognition, delayed recall, and verbal fluency are particularly affected. Studies should identify interventions to improve sleep patterns and quality in this population.

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.

BACKGROUND:The Omicron BA.5 subvariant of SARS-CoV-2 markedly escapes neutralizing antibodies induced by vaccination due to mutations in the Spike (S) protein. Solid organ transplant recipients (SOTRs) suffer high COVID-19 morbidity and demonstrate poor Omicron strain recognition after COVID-19 vaccination. T cell responses may provide a crucial second line of defense. Therefore, it is critical to understand which vaccine regimens induce robust, conserved T cell responses. METHODS:We evaluated anti-S IgG titers, subvariant pseudo-neutralization, and S-specific CD4+ and CD8+ T cell responses from SOTRs in a national, prospective observational trial (n=75). Participants were selected if they received 3 doses of mRNA (homologous boosting) or two doses of mRNA followed by Ad26.COV2.S (heterologous boosting). RESULTS:Homologous boosting with three mRNA doses induced the highest anti-S IgG titers. However, antibodies induced by both vaccine regimens demonstrated significantly lower pseudo-neutralization against BA.5 compared to the ancestral strain. In contrast, vaccine-induced S-specific T cells maintained cross-reactivity against BA.5 compared to ancestral recognition. Homologous boosting induced higher frequencies of activated polyfunctional CD4+ T cell responses, with polyfunctional IL-21+ peripheral T follicular helper cells increased in mRNA-1273 compared to BNT¬¬162b2. IL-21+ cells robustly correlated with antibody titers. Heterologous boosting with Ad26.COV2.S did not increase CD8+ responses compared to homologous boosting. CONCLUSIONS:These data demonstrate that boosting with the ancestral strain can induce cross-reactive T cell responses against emerging variants of concern in SOTRs, but alterative vaccine strategies are required to induce robust CD8+ T cell responses. TRIAL REGISTRATION/BACKGROUND:IRB00248540FUNDING. U01AI138897, U54CA260492, Emory COVID-19 research repository.

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.

BACKGROUND:Statins are the third most prescribed drug class in kidney transplant recipients as cardiovascular disease is the leading cause of death in this population. However, statins' safety profile remains unclear in kidney transplant recipients who are uniquely burdened by concomitant immunosuppression and comorbidities. We conducted a national study to characterize the association of statin use with adverse events in kidney transplant recipients. METHODS:We studied adult (18 years or older) single-organ kidney transplant recipients in 2006-2016 with Medicare as primary payer ( n =57,699). We used prescription drug claims to capture statin use and International Classification of Diseases 9/10 diagnosis codes to capture statin-related adverse events (post-transplant diabetes mellitus, hemorrhagic stroke, cataract, liver injury, and rhabdomyolysis). We conducted multivariable Cox regression for each outcome with statin use as a time-varying exposure. RESULTS:Post-transplant diabetes mellitus was the most common outcome (5-year Kaplan-Meier incidence; 43% in statin users versus 35% in nonusers), followed by cataract (22% versus 12%), liver injury (2% versus 3%), hemorrhagic stroke (1.9% versus 1.4%), and rhabdomyolysis (1.5% versus 0.9%). In our multivariable analysis, statin use was associated with higher hazard of post-transplant diabetes mellitus (adjust hazard ratio [aHR], 1.12; 95% confidence interval [95% CI], 1.07 to 1.18), cataract (aHR, 1.22; 95% CI, 1.14 to 1.31), and rhabdomyolysis (aHR, 1.37; 95% CI, 1.10 to 1.71) but lower hazard of liver injury (aHR, 0.82; 95% CI, 0.71 to 0.95). Statin use was not associated with hemorrhagic stroke (aHR, 1.04; 95% CI, 0.86 to 1.26). CONCLUSIONS:Statins seem to be generally well tolerated in kidney transplant recipients. However, statin use might be associated with slightly higher risk of post-transplant diabetes mellitus, cataract, and rhabdomyolysis.

BACKGROUND:Biliary complications (BCs) continue to impact patient and graft survival after liver transplant (LT), despite improvements in organ preservation, surgical technique, and posttransplant care. Real-world evidence provides a national estimate of the incidence of BC after LT, implications for patient and graft outcomes, and attributable cost not available in transplant registry data. METHODS:An administrative health claims-based BC identification algorithm was validated using electronic health records (N = 128) and then applied to nationally linked Medicare and transplant registry claims. RESULTS:The real-world evidence algorithm identified 97% of BCs in the electronic health record review. Nationally, the incidence of BCs within 1 y of LT appears to have improved from 22.2% in 2002 to 20.8% in 2018. Factors associated with BCs include donor type (living versus deceased), recipient age, diagnosis, prior transplant, donor age, and donor cause of death. BCs increased the risk-adjusted hazard ratio (aHR) for posttransplant death (aHR, 1.43; P  < 0.0001) and graft loss (aHR, 1.48; P  < 0.0001). Nationally, BCs requiring intervention increased risk-adjusted first-year Medicare spending by $39 710 ( P  < 0.0001). CONCLUSIONS:BCs remain an important cause of morbidity and expense after LT and would benefit from a systematic quality-improvement program.

Repeat kidney transplantation (re-KT) is the preferred treatment for patients with graft failure. Changing allocation policies, widening the risk profile of recipients, and improving dialysis care may have altered the survival benefit of a re-KT. We characterized trends in re-KT survival benefit over 3 decades and tested whether it differed by age, race/ethnicity, sex, and panel reactive assay (PRA). By using the Scientific Registry of Transplant Recipient data, we identified 25 419 patients who underwent a re-KT from 1990 to 2019 and 25 419 waitlisted counterfactuals from the same year with the same waitlisted time following graft failure. In the adjusted analysis, a re-KT was associated with a lower risk of death (adjusted hazard ratio [aHR] = 0.63; 95% confidence interval [CI], 0.61-0.65). By using the 1990-1994 era as a reference (aHR = 0.77; 95% CI, 0.69-0.85), incremental improvements in the survival benefit were noted (1995-1999: aHR = 0.72; 95% CI, 0.67-0.78: 2000-2004: aHR = 0.59; 95% CI, 0.55-0.63: 2005-2009: aHR = 0.59; 95% CI, 0.56-0.63: 2010-2014: aHR = 0.57; 95% CI, 0.53-0.62: 2015-2019: aHR = 0.64; 95% CI, 0.57-0.73). The survival benefit of a re-KT was noted in both younger (age = 18-64 years: aHR = 0.63; 95% CI, 0.61-0.65) and older patients (age ≥65 years: aHR = 0.66; 95% CI, 0.58-0.74; P_interaction = .45). Patients of all races/ethnicities demonstrated similar benefits with a re-KT. However, it varied by the sex of the recipient (female patients: aHR = 0.60; 95% CI, 0.56-0.63: male patients: aHR = 0.66; 95% CI, 0.63-0.68; P_interaction = .004) and PRA (0-20: aHR = 0.69; 95% CI, 0.65-0.74: 21-80: aHR = 0.61; 95% CI, 0.57-0.66; P_interaction = .02; >80: aHR = 0.57; 95% CI, 0.53-0.61; P_interaction< .001). Our findings support the continued practice of a re-KT and efforts to overcome the medical, immunologic, and surgical challenges of a re-KT.

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.