AbstractBackgroundWe aimed to develop a tool for predictingHNF1Bmutations in children with congenital abnormalities of the kidneys and urinary tract (CAKUT).MethodsThe clinical and laboratory data from 234 children and young adults with knownHNF1Bmutation status were collected and analyzed retrospectively. All subjects were randomly divided into a training (70%) and a validation set (30%). A random forest model was constructed to predictHNF1Bmutations. The recursive feature elimination algorithm was used for feature selection for the model, and receiver operating characteristic curve statistics was used to verify its predictive effect.ResultsA total of 213 patients were analyzed, includingHNF1B-positive (mut + ,n= 109) andHNF1B-negative (mut − ,n= 104) subjects. The majority of patients had mild chronic kidney disease. Kidney phenotype was similar between groups, but bilateral kidney anomalies were more frequent in the mut + group. Hypomagnesemia and hypermagnesuria were the most common abnormalities in mut + patients and were highly selective ofHNF1B. Hypomagnesemia based on age-appropriate norms had a better discriminatory value than the age-independent cutoff of 0.7 mmol/l. Pancreatic anomalies were almost exclusively found in mut + patients. No subjects had hypokalemia; the mean serum potassium level was lower in theHNF1Bcohort. The abovementioned, discriminative parameters were selected for the model, which showed a good performance (area under the curve: 0.85; sensitivity of 93.67%, specificity of 73.57%). A corresponding calculator was developed for use and validation.ConclusionsThis study developed a simple tool for predictingHNF1Bmutations in children and young adults with CAKUT.Graphical abstract