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Maintenance hemodialysis (HD) patients’ morbidity and mortality remain unacceptably high. It is important to identify risk factors affecting outcome and define their relative contribution. Methods: The data of 93 patients who started HD at the Sheffield Kidney Institute between January 1997 and June 1998 were analyzed retrospectively. Potential risk predictors of 5-year survival were evaluated both at baseline and at 1-year follow-up, including changes from baseline. Results: Patients median age was 60 years (range: 27-80), 75% were males and 23% were diabetics. According to the Index of Co-Existing Disease (ICED) score, 18% of patients were level 0-1 (low comorbidity), 56% level 2 and 26% level 3. The 5-year survival was 58%. The Cox models identified eight independent time-adjusted risk factors for 5-year survival, the importance of which as independent predictors was confirmed by logistic regression models. According to the artificial neural network (ANN) models, the relative importance of these factors was as follows: age (26.6%), baseline systolic blood pressure (24.1%), mean Kt/V during the first year (19.7%), baseline ICED score (14.8%), baseline diastolic blood pressure (8.6%), serum calcium change from baseline (2.8%), blood urea change from baseline (2.1%), serum creatinine change from baseline (1.3%). This ANN model had a high level of predictive performance as assessed by accuracy (93.3%) and by Receiver Operating Characteristic (ROC) curve analysis (AUC=0.92). Conclusion: Integrated use of regression analysis and probabilistic models allow computation of individual risk of mortality in HD. This may help in optimizing care and costs.  Key Words: hemodialysis, risk factors, ICED, artificial neural network

Five Years Survival on Hemodialysis Predicted by Artificial Neural Network Model