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The objective is to assess cardiovascular (CV), malignancy, infectious, graft outcomes and tacrolimus levels for the Indigenous patients compared to Whites after kidney transplant (KTx). 165 Indigenous and 165 White patients matched for the KTx year at Mayo Clinic Arizona from 2007-2015 were studied over a median follow-up of 3 years. Propensity score was calculated to account for baseline differences. Compared to Whites, Indigenous patients had the following characteristics: younger age, more obesity, diabetes, hypertension, and required dialysis prior to KTx (p<0.01). Indigenous patients had longer hospital stay for KTx, shorter follow-up and lived further from the transplant center (p<0.05). 210 (63.6%) received deceased donor KTx and more Whites received a living donor KTx compared to Indigenous patients (55.2% vs 17.6%, p<0.0001). Post-KTx, there was no difference in the CV event rates. The cumulative incidence of infectious complications was higher among the Indigenous patients (HR 1.81, p = 0.0005, 48.5% vs 38.2%, p = 0.013), with urinary causes as the most common. Malignancy rates were increased among Whites (13.3% vs 3.0%, p = 0.001) with skin cancer being the most common. There was a significant increase in the dose normalized tacrolimus level for the Indigenous patients compared to Whites at 1 months, 3 months, and 1 year post-KTx. After adjustment for the propensity score, there was no statistical difference in infectious or graft outcomes between the two groups but the mean number of emergency room visits and hospitalizations after KTx was significantly higher for Whites compared to Indigenous patients. Compared to Whites, Indigenous patients have similar CV events, graft outcomes and infectious complications after accounting for baseline differences.

The goal is to determine the delays and reduced rates of kidney transplant (KTx) for the Indigenous Americans and variables predictive of these outcomes at a large single transplant center. 300 Indigenous Americans and 300 non-Hispanic white American patients presenting for KTx evaluation from 2012-2016 were studied. Compared to whites, the Indigenous Americans had the following: more diabetes, dialysis, physical limitation and worse socioeconomic characteristics(p<0.01); median difference of 20 day delay from referral to KTx evaluation, 17 day delay from approval to UNOS listing and 126.5 longer delay on the waitlist compared to whites(p<0.001). Of the Indigenous Americans listed, more died, were removed, or were still waiting than transplanted compared to whites (p<0.001). Variables predictive of delay from referral to transplant evaluation included: Indigenous race, distance from transplant center, coronary artery disease, and time on dialysis (p<0.05). Cumulative incidence of waitlisting and KTx was lower for Indigenous Americans (p<0.0001). Independent predictors of decreased likelihood of waitlisting included age, peripheral vascular disease, no caregiver, physical limitation, and illegal drug use history (p<0.05). Variables predictive of lower likelihood of KTx included Indigenous race, percentage of time inactive on the waitlist, no caregiver, and O blood type. Among patients referred and evaluated for KTx, the Indigenous American race was independently associated with significant delays in the KTx process after accounting for co-morbid and socioeconomic factors. Cardiovascular morbidity and physical limitation were identified as important determinants of delay and decreased likelihood of waitlisting. Further quantitative and qualitative work is needed to identify and intervene on modifiable barriers to improve access to KTx for the Indigenous Americans.

OBJECTIVE: New-onset diabetes after kidney transplantation (NODAT) has adverse clinical and economic implications. A risk score for NODAT could help identify research subjects for intervention studies. RESEARCH DESIGN AND METHODS: We conducted a single-center retrospective cohort study using pretransplant clinical and laboratory measurements to construct a risk score for NODAT. NODAT was defined by hemoglobin A1c (HbA1c) ≥6.5%, fasting serum glucose ≥126 mg/dL, or prescribed therapy for diabetes within 1 year posttransplant. Three multivariate logistic regression models were constructed: 1) standard model, with both continuous and discrete variables; 2) dichotomous model, with continuous variables dichotomized at clinically relevant cut points; and 3) summary score defined as the sum of the points accrued using the terms from the dichotomous model. RESULTS: A total of 316 subjects had seven pretransplant variables with P < 0.10 in univariate logistic regression analyses (age, planned corticosteroid therapy posttransplant, prescription for gout medicine, BMI, fasting glucose and triglycerides, and family history of type 2 diabetes) that were selected for multivariate models. Areas under receiver operating curves for all three models were similar (0.72, 0.71, and 0.70). A simple risk score calculated as the sum of points from the seven variables performed as well as the other two models in identifying risk of NODAT. CONCLUSIONS: A risk score computed from seven simple pretransplant variables can identify risk of NODAT.

Introduction. The phenomenon of graft-versus-host disease, a solid organ transplant recipient, is a rare development with a very poor prognosis. Case Presentation. A 40-year-old woman with type 1 diabetes developed cutaneous graft-versus-host disease following second pancreas transplantation. Conclusion. The development of a nonspecific rash in the early posttransplant period following a pancreas transplant warrants suspicion for graft-versus-host disease.

Concerns regarding outcomes and early resource utilization are potential deterrents to broader use of kidneys at risk for delayed graft function (DGF). We assessed outcomes specific to kidneys with DGF that required early readmission following transplant. Three groups were identified: 1) recipients with DGF not requiring readmission, 2) recipients with DGF having an isolated readmission, and 3) recipients with DGF requiring ≥2 readmissions. Most recipients either required a single readmission (26.8%, n = 247) or no readmission (56.1%, n = 517); 17.1% ( n = 158), had ≥2 readmissions. Recipients requiring ≥2 readmissions were likely to be diabetic (53.8%, p = 0.04) and have longer dialysis vintage ( p = 0.01). Duration of DGF was longer with increasing number of readmissions ( p < 0.001). There were no differences in patient survival for those with DGF and 0, 1 and ≥2 readmissions ( p = 0.13). Graft survival, however, was lower for those with ≥2 readmissions ( p < 0.0001). This remained true when accounting for death-censored graft loss ( p = 0.0012). Additional subgroup analysis was performed on mate kidneys with and without DGF and mate kidneys, both with DGF, with and without readmissions. For these subgroups, there were no differences in patient or graft survival. As a whole, patients with DGF have excellent outcomes, however, patients with DGF requiring ≥2 readmissions have lower graft survival. A better understanding of recipient variables contributing to multiple readmissions may allow for improvements in the utilization of DGF at-risk kidneys.

Aim: To examine the roles of hospital management and health informatics in supporting nurses during crises.Crisis situations place immense strain on nurses, requiring robust support systems to ensure effective care delivery. Hospital management provides leadership, resource allocation, and psychological support, creating an environment conducive to resilience and efficiency. Health informatics complements this by offering technological tools such as real-time data, workflow automation, and communication platforms to enhance decision-making and coordination. Together, these systems enable nurses to navigate challenges, prioritize patient care, and maintain operational stability. Integrating these approaches is essential for optimizing nurse performance and well-being during crises.