Predicting Outcomes of Renal Transplantation


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Abstract

Aim. To identify the main predictors of patient and allograft survival and devise mathematical models to predict the outcomes after renal allotransplantation (ATP). Material and methods. Observational data of 350 patients undergoing kidney transplantation were analyzed. The post-transplant follow-up averaged 79.1±3.4 months. 229 (65.4%) patients were males. Mean age of the patients was 37.i±0.6 years. 342 (97.7%) patients underwent cadaveric kidney transplantation. Three protocols of immunosuppressive therapy (1ST) were used: tacrolimus+prednisone+azathioprine; tacrolimus+prednisone+myco phenolate; tacrolimus+prednisone+mycophenolate. Part of the patients (59.4%) received prophylactic antiviral treatment with valganciclovir. The following outcomes of kidney transplantation were examined: 1 - the patient is alive with a functioning graft, 2 - the patient is alive, but resumed dialysis due to graft failure, 3 - death. Analysis of patient and allograft survival was conducted. Any graft failure was estimated as a loss of the graft. The mathematical analysis was conducted using SPSS statistical software. The main statistical methods were descriptive statistics, tests for comparing means, the correlation analysis and survival analysis (Cox regression). Results. Cox regression model was used to identify significant predictors (p<0.05) of ATP outcomes for the patient's life: hemoglobin levels before and 1 month after the ATP, albumin level before surgery, alt level 1 month after the ATP, prophylactic antiviral therapy, method of immunological selection, smoking after ATP and compliance. Significant predictors (p<0.05) of ATP results for the graft functioning were: hemoglobin level before ATP, ALT level 1 month after the ATP, smoking before ATP, method of immunological selection, prophylactic antiviral therapy and compliance.

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