Tumor grade and size are associated with recurrence-free survival in duodenal neuroendocrine tumors. When feasible, a less aggressive surgical approach to treat low-grade and low-stage duodenal NETs should be considered.

Background: Current therapy for Type 1 diabetes (T1D) is characterized by significant glucose variability (GV). Pancreas transplantation (PT) is performed in certain T1D patients with and without end-stage renal disease. To date, GV has been examined to a limited extent after PT. Methods:We investigated GV using continuous glucose monitoring (CGM) 3-6 weeks after PT. Results: Eleven patients had simultaneous kidney pancreas transplantation (SPK),nine pancreas after kidney (PAK), and six pancreas transplantation alone (PTA). Mean CGM showed no difference between SPK, 126.5 ± 13.9, PAK 119.9 ± 12.8, and PTA 131.1 ± 29 mg/dL (P value .6). Percentage of time in range (TIR, 70-180 mg/dL) was 92% for SPK, 93.4% in PAK, and 88.5% in PTA with only 0.3%, 1.5%, and 0.3% of time <70 mg/dL. Percentage >180 mg/dL was 7.9% for SPK, 4.9% PAK, and 11% in PTA. Other measures of GV were similar in the three cohorts. In six patients, CGM was performed before and after PT and improved significantly. GV was also better compared with a matched cohort of T1D patients. Conclusions: All 3 types of PT resulted in excellent glucose control 3-6 weeks postprocedure. CGM outcomes represent an important objective outcome after PT. K E Y W O R D S continuous glucose monitor, glycemic variability, hyperglycemia and hypoglycemia, pancreas transplantation, Type 1 diabetes S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Dadlani V, Kaur RJ, Stegall M, et al. Continuous glucose monitoring to assess glycemic control in the first 6 weeks after pancreas transplantation. Clin

Background Transplant candidates are reluctant to accept kidneys from high Kidney Donor Profile Index (KDPI) donors. Incomplete understanding can lead to transplant delays for older transplant candidates. Patients need access to understandable information to make more informed decisions about KDPI. Methods We developed a KDPI‐specific animation with input from six stakeholder groups and conducted a one‐group pre‐post study with 60 kidney transplant candidates for feasibility and acceptability to improve participant KDPI knowledge, understanding, decisional self‐efficacy, and willingness to accept a KDPI > 85% kidney. Data were compared using McNemar's test and Wilcoxon signed‐rank test. Results Compared with pre‐animation scores, post‐animation scores were significantly higher for KDPI knowledge for the entire cohort (4.6 vs 6.1, P < .001) and across different levels of age, educational attainment, health literacy, vintage, and technology access. The frequency of positive responses increased pre‐post animation for KDPI understanding (55% vs 83%, P < .001) and decisional self‐efficacy (47% vs 75%, P < .001). However, willingness to accept KDPI > 85% kidneys (32% vs 36%, P = .83) increased by 2%. After viewing simplifyKDPI, >90% indicated positive ratings on ease of watching, understanding, and engaging. Conclusion In collaboration with stakeholders, an educational animation about KDPI was developed that was well‐received and is promising to impact knowledge.

Plasma cells (PCs) are a major source of alloantibody in transplant patients and are resistant to current therapy. Because receptor-ligand interactions in stromal microenvironments play important roles in the localization, development, and survival of normal PCs, we hypothesized that interfering with CXCR4/CXCL12 interactions with plerixafor might cause PC depletion and enhance the efficacy of the proteasome inhibitor bortezomib. PCs in mouse spleen, bone marrow, and peripheral blood demonstrated CXCR4 expression. We then treated with plerixafor in doses ranging from 240 μg/kg in a single dose to a 1-mg/kg daily dose for 10 days. CXCR4/CXCL12 blockade with plerixafor resulted in increased mobilization of PCs into the peripheral blood. Splenectomy completely abrogated this effect, suggesting that all plerixafor-mobilized cells were from the spleen. The total number of PCs in the spleen and marrow remained constant despite treatment with plerixafor. Bortezomib caused a reduction in PCs, but adding plerixafor did not increase killing. We conclude that CXCR4/CXCL12 interactions are important for the retention of a subpopulation of PCs in the spleen, but this interaction has minimal effect on PCs in the marrow. The lack of enhancement of bortezomib-mediated depletion suggests that factors other than CXCR4/CXCL12 interactions are responsible for drug resistance.

Obesity-related liver disease has become one of the most common indications for liver transplantation and further research is needed to determine the role of bariatric surgery in the optimal management of this population.

BackgroundThere is no mechanism that matches hard-to-place kidneys with the most appropriate candidate. Thus, unwanted kidney offers are typically to recipients with long renal replacement time (vintage) which is a strong risk factor for mortality and graft failure, and in combination with prolonged cold ischemia time (CIT), may promote interactive effects on outcomes.MethodsConsecutive adult isolated kidney transplants between October 2015 and December 2017 were stratified by vintage younger than 1 year and CIT longer than 30 hours.ResultsLong (n = 169) relative to short (n = 93) vintage recipients were significantly more likely to be younger (32.2 years vs 56.9 years, P = 0.02), black race (40.8% vs 18.3%, P = 0.02), have higher estimated posttransplant survival (52.6 vs 42.0, P = 0.04), and have a comorbid condition (45.6% vs 30.1%, P = 0.02); they were less likely to receive a donation after circulatory death kidney (27.8% vs 39.8%, P = 0.05). Long vintage was significantly associated with length of stay longer than 4 days (45.5% vs 30.1%, P = 0.02), and 30-day readmission (37.3% vs 22.6%, P = 0.02) but not additional operations (17.8% vs 15.1%, P = 0.58), short-term patient mortality (3.0% vs 2.2%, P = 0.70), or overall graft survival (P = 0.23). On multivariate logistic regression, long vintage remained an independent risk factor for 30-day readmission (adjusted odds ratio, 1.92; 95% confidence interval, 1.06-3.47); however, there was no interaction of vintage and CIT for this outcome (P = 0.84).ConclusionsReadmission is significantly associated with pretransplant dialysis duration; however, CIT is not a modifying factor for this outcome.

Predicting which renal allografts will fail and the likely cause of failure is important in clinical trial design to either enrich patient populations to be or as surrogate efficacy endpoints for trials aimed at improving long-term graft survival. This study tests our previous Birmingham-Mayo model (termed the BirMay Predictor) developed in a lowrisk kidney transplant population in order to predict the outcome of patients with donor specific alloantibody (DSA) at the time of transplantation and identify new factors to improve graft loss prediction in DSA+ patients. We wanted define ways to enrich the population for future therapeutic intervention trials. The discovery set included 147 patients from Mayo Cohort and the validation set included 111 patients from the Paris Cohort-all of whom had DSA at the time of transplantation. The BirMay predictor performed well predicting 5-year outcome well in DSA+ patients (Mayo C statistic = 0.784 and Paris C statistic = 0.860). Developing a new model did not improve on this performance. A high negative predictive value of greater than 90% in both cohorts excluded allografts not destined to fail within 5 years. We conclude that graft-survival models including histology predict graft loss well, both in DSA+ cohorts as well as DSA-patients. K E Y W O R D S alloantibody, clinical research/practice, kidney (allograft) function/dysfunction, kidney transplantation/nephrology, pathology/histopathology, protocol biopsy, risk assessment/risk stratification 1 | INTRODUC TI ON Predicting which renal allografts will fail and the likely cause of failure is important in clinical trial design to either enrich patient populations to be treated (eg, studies design to treat antibody-mediated rejection [ABMR]) or as surrogate efficacy endpoints for trials aimed at improving long-term graft survival. Several studies have demonstrated that kidney transplant recipients who have DSA at the time of transplantation have inferior outcomes to those without DSA. 1-3 Clearly, new therapy is needed to overcome the immunologic hurdle of preformed

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