Nationwide epidemiology and clinical practice patterns of pediatric urinary tract infections: application of multivariate time-series clustering.

BACKGROUND: The nationwide epidemiology and clinical practice patterns for younger children hospitalized with urinary tract infections (UTIs) were unclear.

METHODS: We conducted a retrospective observational study consisting of 32,653 children aged < 36 months who were hospitalized with UTIs from 856 medical facilities during fiscal years 2011-2018 using a nationally representative inpatient database in Japan. We investigated the epidemiology of UTIs and changes in clinical practice patterns (e.g., antibiotic use) over 8 years. A machine learning algorithm of multivariate time-series clustering with dynamic time warping was used to classify the hospitals based on antibiotic use for UTIs.

RESULTS: We observed marked male predominance among children aged < 6 months, slight female predominance among children aged > 12 months, and summer seasonality among children hospitalized with UTIs. Most physicians selected intravenous second- or third-generation cephalosporins as the empiric therapy for treating UTIs, which was switched to oral antibiotics during hospitalizations for 80% of inpatients. Whereas total antibiotic use was constant over the 8 years, broad-spectrum antibiotic use decreased gradually from 5.4 in 2011 to 2.5 days of therapy per 100 patient-days in 2018. The time-series clustering distinctively classified 5 clusters of hospitals based on antibiotic use patterns and identified hospital clusters that preferred to use broad-spectrum antibiotics (e.g., antipseudomonal penicillin and carbapenem).

CONCLUSIONS: Our study provided novel insight into the epidemiology and practice patterns for pediatric UTIs. Time-series clustering can be useful to identify the hospitals with aberrant practice patterns to further promote antimicrobial stewardship. A higher resolution version of the Graphical abstract is available as Supplementary information.