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.
Keywords: antibiotic prophylaxis; machine learning; urinary tract infections; vesico-ureteral reflux.