Department of Urology, Boston Children's Hospital , Boston , Massachusetts.
Operations Research Center, Massachusetts Institute of Technology , Cambridge , Massachusetts.
J Urol. 2019 Jul;202(1):144-152. doi: 10.1097/JU.0000000000000186. Epub 2019 Jun 7.
Significant debate persists regarding the appropriate workup in children with an initial urinary tract infection. Greatly preferable to all or none approaches in the current guideline would be a model to identify children at highest risk for a recurrent urinary tract infection plus vesicoureteral reflux to allow for targeted voiding cystourethrogram while children at low risk could be observed. We sought to develop a model to predict the probability of recurrent urinary tract infection associated vesicoureteral reflux in children after an initial urinary tract infection.
We included subjects from the RIVUR (Randomized Intervention for Children with Vesico-Ureteral Reflux) and CUTIE (Careful Urinary Tract Infection Evaluation) trials in our study, excluding the prophylaxis treatment arm of the RIVUR. The main outcome was defined as recurrent urinary tract infection associated vesicoureteral reflux. Missing data were imputed using optimal tree imputation. Data were split into training, validation and testing sets. Machine learning algorithm hyperparameters were tuned by the validation set with fivefold cross-validation.
A total of 500 subjects, including 305 from the RIVUR and 195 from the CUTIE trials, were included in study. Of the subjects 90% were female and mean ± SD age was 21 ± 19 months. A recurrent urinary tract infection developed in 72 patients, of whom 53 also had vesicoureteral reflux (10.6% of the total). The final model included age, sex, race, weight, the systolic blood pressure percentile, dysuria, the urine albumin-to-creatinine ratio, prior antibiotic exposure and current medication. The model predicted recurrent urinary tract infection associated vesicoureteral reflux with an AUC of 0.761 (95% CI 0.714-0.808) in the testing set.
Our predictive model using a novel machine learning algorithm provided promising performance to facilitate individualized treatment of children with an initial urinary tract infection and identify those most likely to benefit from voiding cystourethrogram after the initial urinary tract infection. This would allow for more selective application of this test, increasing the yield while also minimizing overuse.
在初始尿路感染的儿童中，适当的检查仍存在很大争议。与当前指南中的全有或全无方法相比，一个能识别复发性尿路感染和膀胱输尿管反流风险最高的儿童的模型将更可取，以便对其进行针对性的排尿性膀胱尿道造影检查，而对于低风险的儿童则可以进行观察。我们试图建立一个模型来预测初始尿路感染后儿童复发性尿路感染相关膀胱输尿管反流的概率。
我们将 RIVUR（膀胱输尿管反流随机干预）和 CUTIE（小心尿路感染评估）试验中的受试者纳入我们的研究，排除了 RIVUR 的预防治疗组。主要结局定义为复发性尿路感染相关的膀胱输尿管反流。使用最优树插补法填补缺失数据。数据被分为训练集、验证集和测试集。通过五重交叉验证对验证集进行超参数调整。
共有 500 名受试者，其中 305 名来自 RIVUR 试验，195 名来自 CUTIE 试验，纳入研究。受试者中 90%为女性，平均年龄为 21 ± 19 个月。72 名患者发生复发性尿路感染，其中 53 名患者也有膀胱输尿管反流（总人数的 10.6%）。最终模型纳入年龄、性别、种族、体重、收缩压百分位数、尿痛、尿白蛋白/肌酐比值、既往抗生素暴露和当前用药。该模型在测试集中对复发性尿路感染相关的膀胱输尿管反流预测的 AUC 为 0.761（95%CI 0.714-0.808）。
我们使用新的机器学习算法的预测模型提供了有希望的性能，以促进对初始尿路感染儿童的个体化治疗，并识别那些最有可能从初始尿路感染后的排尿性膀胱尿道造影中受益的儿童。这将允许更有选择性地应用这项检查，提高收益，同时也减少过度使用。
