Targeted workup after initial febrile Urinary Tract Infection: Using a novel machine learning model to identify children most likely to benefit from VCUG - Bertsimas, Dimitris; Dunn, Jack; Li, Michael; Zhuo, Daisy; Estrada, Carlos; Nelson, Caleb; Scott Wang, Hsin-Hsiao (The Journal of urology, 2019)
Significant debate persists regarding appropriate work-up of children with initial UTI. Greatly preferable to "all-or-none" approaches in current guideline would be a model that can identify children at highest risk for both recurrent UTI and VUR to allow targeted VCUG, while children at low risk could be observed. We sought to develop a model to predict probability of both recurrent UTI and VUR ("rUTI-associated VUR") among children after initial UTI. We included the RIVUR and CUTIE subjects, excluding the prophylaxis treatment arm from RIVUR. The main outcome was defined as rUTI-associated VUR. Missing data was imputed using optimal tree imputation. Data were split into training/validation/testing sets. Machine learning algorithm hyperparameters were tuned by the validation set with 5-fold cross validation. 500 subjects (305 RIVUR and 195 CUTIE) were included in the study (90% female). Mean age was 21±19 months. 72 patients developed rUTI, of whom 53 also had VUR (10.6% of total). The final model included age, gender, race, weight, systolic blood pressure percentile, dysuria, urine albumin/creatinine ratio, prior antibiotics exposure, and current medication. The model predicted rUTI-associated VUR with AUC at 0.761 (95CI: 0.714- 0.808) on the testing set. Our predictive model using a novel machine learning algorithm provides promising performance to facilitate individualized management of children with initial UTI, and identify those most likely to benefit from VCUG after the initial UTI. This would allow more selective use of this test, increasing the yield while also minimizing overutilization.