Development of a tool for predicting HNF1B mutations in children with congenital anomalies of the kidneys and urinary tract – a retrospective multicenter study

Abstract Background The diagnosis of HNF1B disease is a challenge. We aimed to developa tool for predicting HNF1B mutations in children with congenital abnormalities of the kidneys and urinary tract (CAKUT). Methods The clinical and laboratory data from 234 children and young adults with known HNF1B mutation 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 predict HNF1B mutations. The recursive feature elimination algorithm was used for feature selection into the model, and receiver operating characteristiccurve statistics were used to verify its predictive effect. Results 213 patients were analyzed, including HNF1B ‑positive (mut+, n =109) and HNF1B ‑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 of HNF1B . 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 the HNF1B cohort. The above‑mentioned, 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. Conclusion This study developed a simple tool for predicting HNF1B mutations in children with CAKUT.