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Factors affecting survival in kidney recipients at kermanshah
Mohammad Rezaei1, A Kazemnejad, Ar Bardideh
1Department of Medical Statistics, Kermanshah University of Medical Sciences, Kermanshah, Iran. rezaei39@yahoo.com.
Introduction:
Our aim was to evaluate patient and graft survivals in kidney recipients and factors impacting on survival rates at Kermanshah.
Materials And Methods:
This study was done on 712 kidney transplants from 1989 through 2001 in Kermanshah. One of the most important applications of survival analysis is assessing the role of explanatory factors in the studied event. In this study Kaplan-Meier method was used to calculate patient and graft survivals and in order to determine the factors affecting survival, Cox proportional hazard model was used. The iterations in Cox model was four times and the inclusion and exclusion criteria, calculated by forward conditional method were less than 5% and 10%, respectively.
Results:
Of the recipients, 47.6% were female and most of them (94.4%) had received kidneys from living unrelated donors. One-year patient survivals in recipients from living unrelated donors (LURD) and living related donors (LRD) were 89.4% and 100%, 3-year survivals were 82% and 97.4%, and 10-year survivals were 61.4% and 72%, respectively. In addition, graft survival rates in one year were 85.6% and 97.4%, in three years were 77.2% and 92.3%, and in 10 years were 33.3% and 60.6% in LURD and LRD, respectively. In Cox model, four factors, including the presence of surgical or other complications, known primary disease, and donor-recipient relationship had significant association with patient survival and seven factors, including the presence of surgical complications, known primary disease, donor-recipient relationship, gender, weight, same side transplanted kidney, and donor's age had significant relationship with graft survival.
Conclusion:
In summary, it can be concluded that patient and donor demographic characteristics and transplantation conditions may affect patient and graft survival. With the use of multivariate regression analysis methods, the characteristics that have high probability for survival can be determined. Controlling these situations, where they have high survival probability, effectively help better treatment and high survival rate.
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