Background. Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. Methods. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m2 of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Results. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Conclusions. Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.

Malignancy is a common complication after renal transplantation. However, limited data are available on post-transplant malignancy in living kidney transplantation. Therefore, we made a plan to evaluate the incidence and types of malignancies, association with the main risk factors and patient survival in a large population of living kidney transplantation. We conducted a large retrospective multicenter study on 12525 renal recipients, accounting for up to 59% of all kidney transplantation in Iran during 22 years follow up period. All information was collected from observation of individual notes or computerized records for transplant patients. Two hundred and sixty-six biopsy-proven malignancies were collected from 16 Transplant Centers in Iran; 26 different type of malignancy categorized in 5 groups were detected. The mean age of patients was 46.2±12.9 years, mean age at tumor diagnosis was 50.8±13.2 years and average time between transplantation and detection of malignancy was 50.0±48.4 months. Overall tumor incidence in recipients was 2%. Kaposis' sarcoma was the most common type of tumor. The overall mean survival time was 117.1 months (95% CI: 104.9-129.3). In multivariate analysis, the only independent risk factor associated with mortality was type of malignancy. This study revealed the lowest malignancy incidence in living unrelated kidney transplantation.

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