Renal transplantation in Iran over the past two decades: a trend analysis.

BACKGROUND: In this study, we have reported updated statistics of the Iranian Transplantation Registry, the status of the recipients and grafts, and a detailed time trend with respect to patient characteristics. METHODS: We retrospectively reviewed the Iranian Renal Transplantation Registry and information from the Dialysis and Transplant Patients Public Association, to obtain data on all kidney transplantations performed in Iran between 1986 and 2005. Data were gathered regarding the total number of transplantations, graft loss, recipient death, and donor and recipient characteristics, including demographic data, cause of end-stage renal disease (ESRD), and source of kidney. We assessed changes in variables on a biannual basis. RESULTS: A total of 19521 transplantations were registered over the study period, of which, 761 recipients (3.9%) had died and 2333 allografts (11.9%) had been lost. The source of the kidney in 2556 (13%) subjects was a living related donor (LRD), in 16234 (83%) a living unrelated donor (LURD), and in 831 (4%) cadaveric. During the study decades we noted an increase in the number of kidney transplantations (from 22 to 3690), age of recipients (from 30 to 40), male-to-female ratio of recipients (from 0.58 to 0.67), male-to-female ratio of donors (from 0.48 to 0.52), diabetes mellitus (from 0% to 27%), and hypertension (from 4% to 15%), as causes of ESRD, as well as the use of cadaveric kidneys (0% to 11%). CONCLUSION: Analyzing renal transplantation data not only helps to evaluate the effectiveness of transplantation activities in a country, but also provides information to estimate future costs in the health care system.

Data-entropy analysis of renal transplantation data.

INTRODUCTION: The terms entropy and robustness are currently used by biomedical investigators to predict the risk of change in a system. The former is the mathematical identification of uncertainty about a system, while the latter is the likelihood of system stability. We conducted an entropy-based analysis of our renal transplantation data set. MATERIALS AND METHODS: The input variables in our model included donors and recipients, past medical history, and other clinical data. The output variables were 6- month, 1-year, and 2- year patient and graft survivals. Data-entropy analysis was performed with Ontonix s.r.l. software (www.ontonix.com). RESULTS: The total input and output entropy was 13.14 and 1.54, respectively. The mean input and output robustness was 39.14% and 29.54%. The robustness amplification index was 0.75. The minimum entropy of the input variables was reported for a history of myocardial infarction (0.07), vascular disease (0.1), bladder residual (0.13), or urologic surgery (0.15). The minimum entropy of the output variables was 0.20 for 6-month patient survival; 0.22 for 1-year patient survival; 0.25 for 6-month graft survival; 0.27 for 1-year graft survival; 0.28 for 2-year patient survival; and 0.32 for 2-year graft survival. CONCLUSION: Data-entropy analysis demonstrated a high stability of our transplantation data set. Nevertheless, long-term outcomes, especially those of graft survival, were slightly more unpredictable.

Hospitalization databases: a tool for transplantation monitoring.

INTRODUCTION: We sought to account for changes in posttransplant hospitalization patterns in terms of the changes in demographic and transplantation-related variables. METHODS AND MATERIALS: We retrospectively analyzed 1860 cases of kidney transplantation performed between 1992 and 2004 in terms of demographic and transplantation-related variables. Of the 1860 cases, rehospitalization records in the first year posttransplantation were available for 1152 cases, which were assessed for causes of admission, mortality, graft loss, length of stay, and hospital charges. RESULTS: The pattern of rehospitalizations showed the following trends: (1) Increased rate of infection; (2) Decreased rate of graft rejection; and (3) Peak costs of rehospitalization between 1999 and 2000. CONCLUSION: We believed that the increased infection rate and decreased rejection rate may have been related at least partly to the shift in the treatment protocol from azathioprine-based to mycophenolate mofetil regimens in 2000. Furthermore, the peak in the relative frequency of diabetes mellitus and hypertension as the etiology of end-stage renal disease among those having undergone transplantation between 1999 and 2000 may have been responsible for the peak in rehospitalization costs and length of hospital stay. We are strongly of the opinion that hospital statistics are a valuable tool for health care policymakers to monitor transplantation outcomes.