Assessment of Arterial Stiffness in Stable Heart Transplant Recipients.

INTRODUCTION: Arterial stiffness depends on both genetic and environmental factors. The aim of this study was to assess arterial stiffness in patients after heart transplant. METHODS: The study was conducted between May and June 2017. Fifty patients from the Transplantology Clinic of the Institute of Cardiology in Anin, Warsaw, Poland, were enrolled in the study. Pulse wave velocity (PWV), central systolic blood pressure (CSBP), and central diastolic blood pressure (CDBP) were measured and patients' medical records were also analyzed. RESULTS: In the study, 50 patients aged 57.9 years on average were evaluated, of whom 88% were male patients, with average PWV of 8.94 m/s and an average time after transplant of 9.7 years. The study has shown that age (R = 0.77), total cholesterol concentration (R = 0.22, P = .017) and creatinine concentration (R = 0.34; P = .15) show positive correlation with PWV. CONCLUSIONS: Our data indicates that age has significant impact on arterial stiffness and the type of immunosuppressive drugs and transplant rejection episodes do not impact an increase in arterial stiffness.

Performance of the MDRD, CKD-EPI, and Cockcroft-Gault Formulas in Relation to Nutritional Status in Stable Renal Transplant Recipients.

BACKGROUND: Monitoring of the function of the implanted kidney in renal transplant recipients (RTRs) is one of the superior elements of adequate therapeutic actions. The aim of this study was to assess the conventional and unconventional factors affecting the estimated glomerular filtration rate (eGFR) with the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), and Cockcroft-Gault (C-G) formulas among the RTRs. METHODS: The study included 144 RTRs (mean age 52 years). Clinical and laboratory data were analyzed; eGFR was calculated with MDRD, CKD-EPI, and C-G formulas. We compared the results with MDRD as a reference calculating the percentage of reclassifications of chronic kidney disease (CKD) stages. Nutritional status was assessed with a body composition analyzer, Tanita BC 418. RESULTS: Multivariable linear regression analysis with MDRD and CKD-EPI formula as a dependent variable retained the following independent predictors: hemoglobin (Hb) (B = .365; P = .000), and red blood cell distribution width (RDW) (B = -.191; P = .024). Analysis of variance showed the existence of statistically significant differences (all P for trend <.05) between the CKD-EPI, MDRD, and C-G equations within the total scope of eGFR results (51.2 ± 21.2 vs 47.5 ± 18.7 vs 55.6 ± 20.6, respectively) as well as in quartiles of eGFR. CONCLUSIONS: Our data indicate that (1) with a value of eGFR >60 mL/min/1.73 m(2), the MDRD formula shows values that are on average 11% lower than in the CKD-EPI and C-G formulas; (2) with a value of eGFR <60 mL/min/1.73 m(2), the MDRD and CKD-EPI formulas do not show statistically significant differences.