Department of Pediatrics, University of Zielona Góra, Zielona Góra, Poland.
Medical University of Vienna, Vienna, Austria.
Pediatr Nephrol. 2024 Jun;39(6):1847-1858. doi: 10.1007/s00467-023-06262-9. Epub 2024 Jan 10.
We aimed to develop a tool for predicting HNF1B mutations in children with congenital abnormalities of the kidneys and urinary tract (CAKUT).
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 for the model, and receiver operating characteristic curve statistics was used to verify its predictive effect.
A total of 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 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.
This study developed a simple tool for predicting HNF1B mutations in children and young adults with CAKUT.
本研究旨在开发一种预测先天性肾和尿路异常（CAKUT）患儿 HNF1B 突变的工具。
回顾性收集并分析了 234 名已知 HNF1B 突变状态的儿童和青年患者的临床和实验室数据。所有患者均被随机分为训练集（70%）和验证集（30%）。构建随机森林模型预测 HNF1B 突变。采用递归特征消除算法对模型进行特征选择，采用受试者工作特征曲线统计验证其预测效果。
共分析了 213 例患者，其中 HNF1B 阳性（mut⁺，n=109）和 HNF1B 阴性（mut⁻，n=104）患者。大多数患者患有轻度慢性肾脏病。两组间的肾脏表型相似，但 mut⁺组更常出现双侧肾脏异常。低镁血症和高镁尿症是 mut⁺患者最常见的异常，对 HNF1B 具有高度选择性。基于年龄的正常范围的低镁血症的鉴别价值优于独立于年龄的 0.7mmol/l 截断值。胰腺异常几乎仅见于 mut⁺患者。无低钾血症患者；HNF1B 组的血清钾平均水平较低。选择上述具有鉴别能力的参数构建模型，该模型表现出良好的性能（曲线下面积：0.85；敏感度为 93.67%，特异度为 73.57%）。并开发了相应的计算器用于使用和验证。
本研究开发了一种用于预测 CAKUT 患儿 HNF1B 突变的简单工具。
