Tehran University Medical Journal

Background: While excellent organ quality and ideal transplant conditions eliminate many of the known factors that compromise initial graft function (IGF), slow graft function (SGF), still occurs after living donor kidney transplantation (LDKT). The aim of our current study is determination SGF frequency and its risk factors in LDKT 

Methods: In this prospective study, between April 2004 and March 2006, data were collected on 340 LDKT, in Baghiyattallah Hospital, Tehran. Recipients were analyzed in two groups based on initial graft function (IGF): Creatinine <3 mg/dl 5 day after transplantation, SGF: Creatinine ≥ 3 mg/dl 5 day after transplantation with out dialysis in the first week. Donors' and recipients' characteristics and recipient lab. data were compared in two groups by chi-square, Mann-whitney & independent samples T-test.

Results: The incidence of SGF was 22 (6.2%) and IGF 318 (89.8%), Recipients' BMI in IGF were 22.1±3.9 and in SGF were 25.3±3.8 (P=0.001 95% Cl 1.097-1.401 OR= 1.24). SGF relative frequency in female donors is more than male donors. A multivariate analysis model confirms this significant difference. (P=0.044 95% Cl 1.028-7.971 OR= 2.862). SGF relative frequency in PRA (Panel Reactive Antibody) positive recipients are more than negative ones. A multivariate analysis model confirms this significant difference. (P=0.007 95%Cl 1.755-35.280 OR= 7.849). Recipients' age and donors' BMI are significant in univariate analysis (P=0.002 & P=0.029 respectively) but multivariate analysis model dose not confirm those significance. Serum ca & P & PTH levels don't have significant difference between IGF & SGF. Using calcium channels blockers have not a protective effect.

Conclusions: We conclude that negative PRA and lower recipient BMI have protective effects on SGF. Recipients with female donors have higher chance to develop SGF. We recommend recipients reduce their BMI before transplantation. The male donors are preferred to female ones.

Background: Kidney transplantation had been evaluated in some researches in Iran mainly with clinical approach. In this research we evaluated graft survival in kidney recipients and factors impacting on survival rate. Artificial neural networks have a good ability in modeling complex relationships, so we used this ability to demonstrate a model for prediction of 5yr graft survival after kidney transplantation.
Methods: This retrospective study was done on 316 kidney transplants from 1984 through 2006 in Isfahan. Graft survival was calculated by Kaplan-meire method. Cox regression and artificial neural networks were used for constructing a model for prediction of graft survival.
Results: Body mass index (BMI) and type of transplantation (living/cadaver) had significant effects on graft survival in cox regression model. Effective variables in neural network model were recipient age, recipient BMI, type of transplantation and donor age. One year, 3 year and 5 year graft survival was 96%, 93% and 90% respectively. Suggested artificial neural network model had good accuracy (72%) with the area under the Receiver-Operating Characteristic (ROC) curve 0.736 and appropriate results in goodness of fit test (κ2=33.924). Sensitivity of model in identification of true positive situations was more than false negative situations (72% Vs 61%).
Conclusion: Graft survival in living donors was more than cadaver donors. Graft survival decreased when the BMI increased at transplantation time. In traditional statistical approach Cox regression analysis is used in survival analysis, this research shows that artificial neural networks also can be used in constructing models to predict graft survival in kidney transplantation.