Top-Down versus Bottom-Up Approach in Children Presenting with Urinary Tract Infection: Comparative Effectiveness Analysis Using RIVUR and CUTIE Data.

PURPOSE: The initial imaging approach to children with urinary tract infection (UTI) is controversial. Along with renal/bladder ultrasound, some advocate voiding cystourethrogram (VCUG), ie a bottom-up approach, while others advocate dimercaptosuccinic acid (DMSA) scan, ie a top-down approach. Comparison of these approaches is challenging. In the RIVUR/CUTIE trials, however, all subjects underwent both VCUG and DMSA scan. Our objective was to perform a comparative effectiveness analysis of the bottom-up vs top-down approach. MATERIALS AND METHODS: We simulated 1,000 hypothetical sets of 500 children using RIVUR/CUTIE data. In the top-down approach, patients underwent initial DMSA scan, and only those with renal scarring underwent VCUG. In the bottom-up approach, the initial study was VCUG. We assumed all children with vesicoureteral reflux (VUR) received continuous antibiotic prophylaxis (CAP). Outcomes included recurrent UTI, number of VCUGs and CAP exposure. We assumed a 25% VUR prevalence in children with initial UTI with sensitivity analysis using 40% VUR prevalence. RESULTS: Median age of the original RIVUR/CUTIE cohort was 12 months. First DMSA scan was performed at a median of 8.2 weeks (IQR 5-11.8) after the index UTI. In the simulated cohort, slightly higher yet statistically significantly recurrent UTI was associated with the top-down compared with the bottom-up approach (24.4% vs 18.0%, p=0.045). On the other hand, the bottom-up approach resulted in more VCUG (100% vs 2.4%, p <0.001). Top-down resulted in fewer CAP-exposed patients (25% vs 0.4%, p <0.001) and lower overall CAP exposure (5 vs 162 days/person, p <0.001). Sensitivity analysis was performed with 40% VUR prevalence with similar results. CONCLUSIONS: The top-down approach was associated with slightly higher recurrent UTI. Compared to the bottom-up approach, it significantly reduced the need for VCUG and CAP.

Selecting Children with Vesicoureteral Reflux Who are Most Likely to Benefit from Antibiotic Prophylaxis: Application of Machine Learning to RIVUR.

PURPOSE: Continuous antibiotic prophylaxis reduces the risk of recurrent urinary tract infection by 50% in children with vesicoureteral reflux. However, there may be subgroups in whom continuous antibiotic prophylaxis could be used more selectively. We sought to develop a machine learning model to identify such subgroups. MATERIALS AND METHODS: We used RIVUR data, randomly split into train/test in a 4:1 ratio. Two models were developed to predict recurrent urinary tract infection risk in scenario with and without continuous antibiotic prophylaxis. The test set was then used to validate recurrent urinary tract infection events and the effectiveness of continuous antibiotic prophylaxis. Predicted probabilities of recurrent urinary tract infection were generated from each model. Continuous antibiotic prophylaxis was assigned at various cutoffs of recurrent urinary tract infection risk reduction to evaluate continuous antibiotic prophylaxis effectiveness. RESULTS: A total of 607 patients (558 female/49 male, median age 12 months) were included. Predictors included vesicoureteral reflux grade, serum creatinine, race/gender, prior urinary tract infection symptoms (fever/dysuria) and weight percentiles. The AUC of the prediction model of recurrent urinary tract infection (continuous antibiotic prophylaxis/placebo) was 0.82 (95% CI 0.74-0.87). Using 10% recurrent urinary tract infection risk reduction cutoff, minimal recurrent urinary tract infection per population level can be achieved by giving continuous antibiotic prophylaxis to 40% of patients with vesicoureteral reflux instead of everyone. In a test set (121), 51 patients had continuous antibiotic prophylaxis randomization consistent with model recommendation (continuous antibiotic prophylaxis if recurrent urinary tract infection risk reduction >10%). Recurrent urinary tract infection incidence was significantly lower among this group compared to those whose continuous antibiotic prophylaxis assignment differed from model suggestion (7.5% vs 19.4%, p=0.037). CONCLUSIONS: Our predictive model identifies patients with vesicoureteral reflux who are more likely to benefit from continuous antibiotic prophylaxis, which would allow more selective, personalized use of continuous antibiotic prophylaxis with maximal benefit, while minimizing use in those with least need.

A machine learning algorithm predicting risk of dilating VUR among infants with hydronephrosis using UTD classification.

BACKGROUNDS: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR), management recommendations tend to be subjective. OBJECTIVE: We sought to develop a model to reliably predict VUR from early post-natal ultrasound. STUDY DESIGN: Radiology records from single institution were reviewed to identify infants aged 0-90 days undergoing early ultrasound for antenatal UTD. Medical records were reviewed to confirm diagnosis of VUR. Primary outcome defined as dilating (≥Gr3) VUR. Exclusion criteria include major congenital urologic anomalies (bilateral renal agenesis, horseshoe kidney, cross fused ectopia, exstrophy) as well as patients without VCUG. Data were split into training/testing sets by 4:1 ratio. Machine learning (ML) algorithm hyperparameters were tuned by the validation set. RESULTS: In total, 280 patients (540 renal units) were included in the study (73 % male). Median (IQR) age at ultrasound was 27 (18-38) days. 66 renal units were found to have ≥ grade 3 VUR. The final model included gender, ureteral dilation, parenchymal appearance, parenchymal thickness, central calyceal dilation. The model predicted VUR with AUC at 0.81(0.73-0.88) on out-of-sample testing data. Model is shown in the figure. DISCUSSION: We developed a ML model that can predict dilating VUR among patients with hydronephrosis in early ultrasound. The study is limited by the retrospective and single institutional nature of data source. This is one of the first studies demonstrating high performance for future diagnosis prediction in early hydronephrosis cohort. CONCLUSIONS: By predicting dilating VUR, our predictive model using machine learning algorithm provides promising performance to facilitate individualized management of children with prenatal hydronephrosis, and identify those most likely to benefit from VCUG. This would allow more selective use of this test, increasing the yield while also minimizing overutilization.

High-performance pediatric surgical risk calculator: A novel algorithm based on machine learning and pediatric NSQIP data.

BACKGROUNDS: New methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP). METHODS: All 2012-2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012-2017 data. 2018 data was used as independent performance evaluation. RESULTS: 431,148 patients were included in the 2012-2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications). CONCLUSIONS: We developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.