Assessing risk indicators of allograft survival of renal transplant: An application of joint modeling of longitudinal and time-to-event analysis

Sayyadi, H and Zayeri, F and Baghestani, A.R and Baghfalaki, T and Afshari, A.T and Mohammad Rahimi, M and Fereidoni, J and Makhdoomi, K (2017) Assessing risk indicators of allograft survival of renal transplant: An application of joint modeling of longitudinal and time-to-event analysis. Iranian Red Crescent Medical Journal, 19 (3).

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Abstract

After kidney transplantation, many risk factors can lead to graft rejection and force the patient to return to dialysis treatment. Objectives: This study aims to identify risk indicators of renal graft failure, such as serum creatinine, on long-term graft survival, using a novel statistical technique. Methods: In this historical cohort study, 129 patients who underwent kidney transplants were assessed and followed up from September 2003 to December 2014 in Urmia, Iran. The main outcome of the study was assessing the survival rate of kidney transplant in these subjects. In addition, the serum creatinine levels were measured repeatedly for one year after the operation, as the most important risk indicator of graft failure. In addition, the effect of other indicators on graft survival were assessed using a joint modeling of longitudinal and survival technique, using the R software, version 3.0.2. Results: One-, three-, five-, and ten-year graft survival was 93.8%, 86.8%, 76.6%, and 37.4%, respectively. The results of the joint model showed that risk indicators, such as serum creatinine level (P < 0.0001, HR = 1.82), patient’s age (P = 0.006, HR = 1.03), and antithymocytes globulin (P = 0.019, HR = 2.57) had a significant relationship to graft survival. Conclusions: In general, our study showed that short-term graft failure in Iran is almost equal to the reported rates in some developed countries, but its long-term failure is rather high compared to these same countries. In this context, monitoring the postoperative risk indicators of graft rejection, such as the serum creatinine level, plays an important role in increasing the survival rate of kidney transplantation. The present model can be used to design similarly structured datasets

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