scholarly journals Medical Evaluation of the Adult Kidney Transplant Candidate

Author(s):  
Phuong-Thu Pham ◽  
Son V. ◽  
Phuong-Anh Pham ◽  
Phuong- Chi

Author(s):  
Richard A. Fatica ◽  
Stuart M. Flechner ◽  
Titte R. Srinivas


Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 390
Author(s):  
Camilo G. Sotomayor ◽  
Stan Benjamens ◽  
Hildebrand Dijkstra ◽  
Derya Yakar ◽  
Cyril Moers ◽  
...  

Ultrasound examination is advised for early post-kidney transplant assessment. Grayscale median (GSM) quantification is novel in the kidney transplant field, with no systematic assessment previously reported. In this prospective cohort study, we measured the post-operative GSM in a large cohort of adult kidney transplant recipients (KTR) who consecutively underwent Doppler ultrasound directly after transplantation (within 24 h), compared it with GSM in nontransplanted patients, and investigated its association with baseline and follow-up characteristics. B-mode images were used to calculate the GSM in KTR and compared with GSM data in nontransplanted patients, as simulated from summary statistics of the literature using a Mersenne twister algorithm. The association of GSM with baseline and 1-year follow-up characteristics were studied by means of linear regression analyses. In 282 KTR (54 ± 15 years old, 60% male), the median (IQR) GSM was 55 (45–69), ranging from 22 to 124 (coefficient of variation = 7.4%), without differences by type of donation (p = 0.28). GSM in KTR was significantly higher than in nontransplanted patients (p < 0.001), and associated with systolic blood pressure, history of cardiovascular disease, and donor age (std. β = 0.12, −0.20, and 0.13, respectively; p < 0.05 for all). Higher early post-kidney transplant GSM was not associated with 1-year post-kidney transplant function parameters (e.g., measured and estimated glomerular filtration rate). The data provided in this study could be used as first step for further research on the application of early postoperative ultrasound in KTR.



2019 ◽  
Vol 58 (4) ◽  
pp. 515-524
Author(s):  
Mathilde Tamain ◽  
Johnny Sayegh ◽  
Arnaud Lionet ◽  
Philippe Grimbert ◽  
Carole Philipponnet ◽  
...  


2013 ◽  
Vol 34 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Jinshan Shen ◽  
Robert Townsend ◽  
Xiaoli You ◽  
Yun Shen ◽  
Ping Zhan ◽  
...  


Author(s):  
Troels K. Bergmann ◽  
Stefanie Hennig ◽  
Katherine A. Barraclough ◽  
Nicole M. Isbel ◽  
Christine E. Staatz


2011 ◽  
Vol 58 (6) ◽  
pp. 971-980 ◽  
Author(s):  
Bertram L. Kasiske ◽  
Aleksandra Kukla ◽  
Dolca Thomas ◽  
Jennifer Wood Ives ◽  
Jon J. Snyder ◽  
...  


2018 ◽  
Vol 28 (4) ◽  
pp. 368-375 ◽  
Author(s):  
Tara O’Brien ◽  
Cynthia L. Russell ◽  
Alai Tan ◽  
Mallory Washington ◽  
Donna Hathaway

Introduction: Rapidly growing use of mobile technology provides a platform for self-management of care support for those with chronic conditions. Few studies have explored the characteristics or access patterns of kidney transplant recipients who use mHealth applications (apps) for self-management of care. Research Questions: The primary aim of this study was to describe demographics, use, barriers, and perceptions of mobile apps for self-management of care among adult kidney transplants recipients. The secondary aim was to compare blood urea nitrogen, glomerular filtration rate, and number of hospitalizations among mHealth app users, other app users, and non-app users. Methods: A cross-sectional design was used to administer the Mobile Application Use among Kidney Transplant Recipients Questionnaire. Descriptive statistics, χ2 statistics, and analysis of variance were used for the primary aim and linear regression was used for the secondary aim. Results: The sample included mostly African American males (n = 123, 75.5%) with a mean age of 50 (13.2) years. Knowledge was the greatest barrier reported by the non-app users (mHealth app users 9%, other app users 12%, non-app users, 49%, P < .001). Significantly fewer hospitalizations were found in the mHealth app users compared to other app users (regression coefficient b = −1.2, standard error [SE] = 0.5) and non-app users ( b = −0.9, SE = 0.6), adjusting for patient demographic and clinical characteristics. Discussion: Findings suggest a relationship may exist between mHealth app use and a decrease in the number of hospitalizations following kidney transplantation.



2020 ◽  
Vol 104 (S3) ◽  
pp. S625-S625
Author(s):  
Kanitha Tiankanon ◽  
Natavudh Townamchai ◽  
Stephen Kerr ◽  
Siriwan Thongthip ◽  
Suwasin Udomkarnjananun ◽  
...  


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