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The majority of urinary stones in children are composed of calcium oxalate. To investigate the interaction between urinary calcium, oxalate, and citrate as major risk factors for calcium stones formation, their 24-h urinary excretion was determined in 30 children with urolithiasis and 15 normal healthy children. The cutoff points between children with urolithiasis and healthy children, accuracy, sensitivity, and specificity for each risk factor alone as well as for all three taken together were determined. OneR and J4.8 classifiers as parts of the larger data mining software Weka, based on machine learning algorithms, were used for the determination of the cutoff points for differentiation of the children. The decision tree based on J4.8 classifier analysis of all three risk factors together proved to be the best for differentiating stone formers from normal children. In comparison to the accuracy of the differentiation after calcium and oxalate of 80% and 75.6%, respectively, the decision tree showed an accuracy of 97.8%. Even when its stability was tested by the leave-one-out cross-validation procedure, the accuracy remained at a very acceptable percentage of 93.2% correctly classified patients. J4.8 classifier analysis gave a look inside urinary calcium, oxalate, and citrate interaction. Urinary calcium excretion was shown as the most informative in discrimination of the children with urolithiasis from healthy children. However, it was shown that oxalate and citrate excretions might influence the stone formation in a subpopulation of the stone formers. In patients with low urinary calcium, a major role in lithogenesis belongs to oxalate, in some of them alone and in others in conjunction with citrate. Decreased urinary citrate excretion in the presence of increased oxalate excretion may lead to stone formation.

An imbalance between urinary-promoting and -inhibiting factors has been suggested as more important in urinary stone formation than a disturbance of any single substance. To investigate the value of promoter/inhibitor ratios for estimation of the risk of urolithiasis, urinary citrate/calcium, magnesium/calcium oxalate, and oxalate/citrate × glycosaminoglycans ratios were determined in 30 children with urolithiasis, 36 children with isolated hematuria, and 15 healthy control children. The cutoff points between normal children and children with urolithiasis, accuracy, specificity, and sensitivity for each ratio were determined and compared with those of the 24-h urine calcium and oxalate excretion and urine saturation calculated with the computer program EQUIL 2. The neural network application (aiNET Artificial Neural Network, version 1.25) was used for the determination of the cutoff points for the classification of normal children and the urolithiasis group. The best test for differentiating stone formers from non-stone formers proved the aiNET determined cutoff values of oxalate/citrate × glycosaminoglycans ratio. The method showed 97.78% accuracy, 100% sensitivity, and 93.33% specificity. Two cutoff points between normal and urolithiasis groups were found showing that the children with urolithiasis had ratio values either above 34.00 or less than 10.16. Increased oxalate excretion was linked to the first cutoff value (34.00), and decreased glycosaminoglycans excretion was typical of the second cutoff value (10.16).

To investigate the risk for the development of urolithiasis in 30 children with urolithiasis, 36 children with isolated hematuria, and 15 healthy control children, 24-h urinary excretion of calcium, sodium, oxalate, citrate, sulfate, phosphate, magnesium, urate, chloride, ammonium, and glycosaminoglycans was determined and urine saturation for calcium oxalate was calculated with the computer program EQUIL 2. Compared with controls, children with urolithiasis had significantly increased calcium excretion, oxalate excretion, and urine saturation, whereas children with isolated hematuria had significantly increased calcium excretion only. The best estimation of the relative risk of urolithiasis can be made after urine saturation, using logistic regression. The percentage of patients correctly classified after urine saturation is 85.41% in comparison with 80.95% and 73.81% when the estimation was done by calcium excretion and oxalate excretion, respectively. Using the breakpoint value of 4.29 for urine saturation, it was possible to separate children with increased risk of urolithiasis development from the group of children with isolated hematuria.