A Multimodal Fuzzy Approach in Evaluating Pediatric Chronic Kidney Disease Using Kidney Biomarkers

Chronic kidney disease (CKD) is one of the most important causes of chronic pediatric morbidity and mortality and places an important burden on the medical system. Current diagnosis and progression monitoring techniques have numerous sensitivity and specificity limitations. New biomarkers for monitoring CKD progression have been assessed. Neutrophil gelatinase-associated lipocalin (NGAL) has had some promising results in adults, but in pediatric patients, due to the small number of patients included in the studies, cutoff values are not agreed upon. The small sample size also makes the statistical approach limited. The aim of our study was to develop a fuzzy logic approach to assess the probability of pediatric CKD progression using both NGAL (urinary and plasmatic) and routine blood test parameters (creatinine and erythrocyte sedimentation rate) as input data. In our study, we describe in detail how to configure a fuzzy model that can simulate the correlations between the input variables ESR, NGAL-P, NGAL-U, creatinine, and the output variable Prob regarding the prognosis of the patient's evolution. The results of the simulations on the model, i.e., the correlations between the input and output variables (3D graphic presentations) are explained in detail. We propose this model as a tool for physicians which will allow them to improve diagnosis, follow-up, and interventional decisions relative to the CKD stage. We believe this innovative approach can be a great tool for the clinician and validates the feasibility of using a fuzzy logic approach in interpreting NGAL biomarker results for CKD progression.