Hospitalizations for Cardiovascular Disease After Liver Transplantation in the United States.

Cardiovascular disease (CVD) is a leading cause of post-liver transplant death, and variable care patterns may affect outcomes. We aimed to describe epidemiology and outcomes of inpatient CVD care across US hospitals. Using a merged data set from the 2002-2011 Nationwide Inpatient Sample and the American Hospital Association Annual Survey, we evaluated liver transplant patients admitted primarily with myocardial infarction (MI), stroke (cerebrovascular accident [CVA]), congestive heart failure (CHF), dysrhythmias, cardiac arrest (CA), or malignant hypertension. Patient-level data include demographics, Charlson comorbidity index, and CVD diagnoses. Facility-level variables included ownership status, payer-mix, hospital resources, teaching status, and physician/nursing-to-bed ratios. We used generalized estimating equations to evaluate patient- and hospital-level factors associated with mortality. There were 4763 hospitalizations that occurred in 153 facilities (transplant hospitals, n = 80). CVD hospitalizations increased overall by 115% over the decade (P < 0.01). CVA and MI declined over time (both P < 0.05), but CHF and dysrhythmia grew significantly (both P < 0.03); a total of 19% of hospitalizations were for multiple CVD diagnoses. Transplant hospitals had lower comorbidity patients (P < 0.001) and greater resource intensity including presence of cardiac intensive care unit, interventional radiology, operating rooms, teaching status, and nursing density (all P < 0.01). Transplant and nontransplant hospitals had similar unadjusted mortality (overall, 3.9%, P = 0.55; by diagnosis, all P > 0.07). Transplant hospitals had significantly longer overall length of stay, higher total costs, and more high-cost hospitalizations (all P < 0.05). After risk adjustment, transplant hospitals were associated with higher mortality and high-cost hospitalizations. In conclusion, CVD after liver transplant is evolving and responsible for growing rates of inpatient care. Transplant hospitals are associated with poor outcomes, even after risk adjustment for patient and hospital characteristics, which may be attributable to selective referral of certain patient phenotypes but could also be related to differences in quality of care. Further study is warranted.

Incidence, Risk Factors, and Trends for Postheart Transplantation Diabetes Mellitus.

This retrospective study analyzed glycemic trends, incidence of post-transplant diabetes mellitus (PTDM) incidence and associated risk factors in a cohort of patients who underwent first-time heart transplantation (HT). Univariate analyses compared patient with and without pretransplant diabetes mellitus (DM). Multivariate regression analyses were conducted to determine association between PTDM and different risk factors. Finally, trends in glucometrics and other outcomes are described across follow-up time points. There were 152 patients who underwent HT between 2010 and 2015, 109 of whom had no pretransplant history of DM. PTDM incidence was 38% by the 1-year follow-up. Pretransplant body mass index (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.01 to 1.23, p = 0.03), insulin use during the final 24 hours of inpatient stay (OR 4.26, 95% CI 1.72 to 10.56, p <0.01), mean inpatient glucose (OR 2.21, 95% CI 1.33 to 3.69, p <0.01), and mean glucose in the final 24 hours before discharge (OR 1.29, 95% CI 1.03 to 1.60, p = 0.03) were associated with increased odds of PTDM at 1 year. In patients on insulin before discharge, blood glucose values were significantly higher compared with those who were not (136 mg/dl vs 114 mg/dl at 1 to 3 months, 112 vs 100 at 4 to 6 months, 109 vs 98 at 8 to 12 months, all p <0.01). This analysis improves understanding of PTDM incidence, glucometric trends, and risk differences by DM status in the HT population. Similar to liver and kidney patients, inpatient glucometrics may be informative of PTDM risk in HT patients. Guidelines for this population should be developed to account for risk heterogeneity and need for differential management.