Outcomes of Kidney Allograft and Recipient Survival After Liver Transplantation by Induction Type in the United States.

There are several choices for induction immunosuppression in kidney-after-liver transplantation. We used the Scientific Registry of Transplant Recipients database. We assessed all kidney-after-liver transplant recipients in the United States between 1/1/2000 and 7/31/2017 to study kidney graft and patient outcomes by induction type. We only included patients discharged on tacrolimus and mycophenolate with or without steroids and had a negative crossmatch before kidney engraftment. We grouped recipients by kidney induction type into the following 3 groups: depletional (n = 550), nondepletional (n = 434), and no antibody induction (n = 144). We studied patient and kidney allograft survival using Cox proportional hazard regression, with transplant center included as a random effect. Models were adjusted for liver induction regimen, recipient and donor age, sex, human leukocyte antigen mismatches, payor type, living donor kidney transplantation, dialysis status, time from liver engraftment, hepatitis C virus status, and the presence of diabetes mellitus at time of kidney transplantation and transplantation year. The 6-month and 1-year rejection rates did not differ between groups. Compared with no induction, neither depletional nor nondepletional induction was associated with an improved recipient or graft survival in the multivariable models. Depletional induction at the time of liver transplantation was associated with worse patient survival after kidney transplantation (hazard ratio [HR], 1.7; 95% confidence interval [CI], 1.09-2.67; P = 0.02). Living donor kidney transplantation was associated with a 48.1% improved graft survival (HR, 0.52; 95% CI, 0.33-0.82; P = 0.00). In conclusion, in the settings of a negative cross-match and maintenance with tacrolimus and mycophenolate, induction use was not associated with a patient or graft survival benefit in kidney-after-liver transplantations.

Enteric Conversion of Bladder-drained Pancreas as a Predictor of Outcomes in Almost 600 Recipients at a Single Center.

Complications associated with bladder-drained pancreata necessitating enteric conversion are common. Data on the outcomes after enteric conversion are conflicting. We studied the association between enteric conversion and the pancreas graft rejection, loss, and mortality. METHODS: At our center, 1117 pancreas transplants were performed between 2000 and 2016. We analyzed 593 recipients with bladder-drained pancreata, of which 523 received solitary transplants and 70 received simultaneous pancreas-kidney transplants. Kaplan-Meier function was used to estimate time to conversion by transplant type. Cox proportional hazards models were utilized to evaluate patient survival, death-censored graft survival, and acute rejection-free survival while treating conversion as a time-dependent covariate. Subsequently, we examined the association between timing of conversion and the same outcomes in the conversion cohort. RESULTS: At 10 y posttransplant, 48.8% of the solitary pancreas recipients and 44.3% of simultaneous pancreas-kidney transplant recipients had undergone enteric conversion. The enteric conversion was associated with 85% increased risk of acute rejection (hazard ratio [HR] = 1.85; 95% confidence interval [CI] = 1.37-2.49; P < 0.001). However, the conversion was not associated with graft loss or mortality. In the conversion cohort, a longer interval from engraftment to conversion was associated with an 18% lower rejection rate (HR = 0.82; 95% CI = 0.708-0.960; P = 0.013) and a 22% better graft survival (HR = 0.78; 95% CI = 0.646-0.946; P = 0.01). CONCLUSIONS: Enteric conversion was associated with increased risk of rejection, but not increased risks of graft loss or mortality. The decision to convert should consider the increased rejection risk. A longer interval from engraftment to conversion appears favorable.

Weight gain after kidney donation: Association with increased risks of type 2 diabetes and hypertension.

In the general population, obesity is associated with an increased risk of developing hypertension (HTN), type 2 diabetes mellitus (DM), and end-stage renal disease (ESRD). Therefore, most transplant centers have a body mass index (BMI) threshold for accepting living kidney donors. But there have been no studies of postdonation weight gain trends and any associated risks. We tracked serial BMIs in 940 donors for a median (IQ range) follow-up of 22.3 (15.4-35.8) years. We studied the impact of postdonation weight gain in a model adjusted for family history of HTN or DM. Donor characteristics included age, sex, smoking, fasting blood glucose, eGFR, systolic and diastolic BP, and BMI at time of donation and time postdonation. Postdonation weight gain was associated with a significant increase in the relative risk of developing HTN RR 1.93 (95% CI 1.51-2.46) (P < 0.001) and/or DM RR 4.18 (95% CI 2.05-8.5) (P < 0.0001), but not (to date) cardiovascular disease (CVD), reduced eGFR or death. Like the general population, donors gained weight as they aged; a higher BMI was associated with higher incidence of DM and HTN. Postdonation care should include ongoing counseling on the risks of substantial weight gain.

Comparison of cystatin C and creatinine-based equations for GFR estimation after living kidney donation.

BACKGROUND: The performance of glomerular filtration rate (GFR) equations incorporating both cystatin C (CysC) and serum creatinine (Creat) in living kidney donors has not been studied before. METHODS: From a population of 3,698 living kidney donors, 257 donors were randomly selected to undergo GFR measurement (mGFR) by the plasma disappearance of iohexol. GFR was estimated with the Modification of Diet in Renal Disease (MDRD) equation and the Chronic Kidney Disease Epidemiology Collaboration study eGFR(CKD-EPI-Creat) in 257 donors and the two newly developed equations using CysC with and without Creat, eGFR(CKD-EPI-CysC) and eGFR(CKD-EPI-Creat+CysC), in 215 donors. RESULTS: Mean mGFR was 71.8±11.8 mL/min/1.73 m. The eGFR(MDRD) exhibited least and only negative bias and the three other models were comparable in terms of bias. The eGFR(CKD-EPI-Creat+CysC) equation was most precise; r=0.64. Both eGFR(MDRD) and eGFR(CKD-EPI-Creat+CysC) had high percentage (94.4% and 92.6%, respectively) of estimates falling within 30% of mGFR versus estimates by eGFR(CKD-EPI-Creat) and eGFR(CKD-EPI-CysC) equations (87.2% and 85.1%, respectively). The eGFR(MDRD) was by far most accurate in identifying those with mGFR less than 60 mL/min/1.73 m whereas the CKD-EPI models were extremely accurate in classifying those with mGFR greater than or equal to 60 mL/min/1.73 m. CONCLUSIONS: eGFR(CKD-EPI-Creat+CysC) equation provides comparable accuracy to the eGFR(MDRD) in overall estimation of mGFR, but with higher precision. However, eGFR(CKD-EPI-Creat+CysC) clearly misses many of those with a post-donation GFR less than 60 mL/min/1.73 m and therefore eGFR(MDRD) is preferable in detecting donors with GFR less than 60 mL/min/1.73 m.

Tacrolimus trough levels after month 3 as a predictor of acute rejection following kidney transplantation: a lesson learned from DeKAF Genomics.

Most calcineurin inhibitor (CNI)-based protocols reduce blood trough goals approximately 2-3 months post-transplant in clinically stable kidney transplant recipients. The CNI target trough level to prevent rejection, after reduction, is unknown. Using a multivariate Cox proportional hazards model, we determined the association of time-varying tacrolimus (TAC) trough levels with acute rejection (AR) occurring in the first 6 months post-transplant, but specifically we assessed this association after 3 months. A total of 1930 patients received TAC-based immunosuppression prior to AR in a prospective study. Of the 151 (7.8%) who developed AR, 47 developed AR after 3 months post-transplant. In an adjusted time-varying multivariate model, each 1 ng/ml decrease in TAC trough levels was associated with a 7.2% increased risk of AR [hazards ratio (HR) = 1.07, 95% confidence interval (CI) (1.01, 1.14) P = 0.03] in the first 6 months. There was an additional 23% increased risk of AR with each 1 ng/ml decrease in the TAC trough levels in months 3-6 [HR = 1.23, 95% CI (1.06, 1.43) P = 0.008]. In conclusion, lower TAC trough levels were significantly associated with increased risk of AR in the first 6 months post-transplant with additional risk of AR between months 3 and 6 post-transplant. The timing and practice of TAC dose reduction should be personalized based on the individual's risk factors.