Abstract

Introduction. The terms entropy and robustness are currently used by biomedical investigators to predict the risk of change in a system. The former is the mathematical identification of uncertainty about a system, while the latter is the likelihood of system stability. We conducted an entropy-based analysis of our renal transplantation data set. Materials and Methods. The input variables in our model included donors and recipients, past medical history, and other clinical data. The output variables were 6- month, 1-year, and 2- year patient and graft survivals. Data-entropy analysis was performed with Ontonix s.r.l. software (www.ontonix.com). Results. The total input and output entropy was 13.14 and 1.54, respectively. The mean input and output robustness was 39.14 and 29.54. The robustness amplification index was 0.75. The minimum entropy of the input variables was reported for a history of myocardial infarction (0.07), vascular disease (0.1), bladder residual (0.13), or urologic surgery (0.15). The minimum entropy of the output variables was 0.20 for 6-month patient survival; 0.22 for 1-year patient survival; 0.25 for 6-month graft survival; 0.27 for 1-year graft survival; 0.28 for 2-year patient survival; and 0.32 for 2-year graft survival. Conclusion. Data-entropy analysis demonstrated a high stability of our transplantation data set. Nevertheless, long-term outcomes, especially those of graft survival, were slightly more unpredictable.