1. The study reports the development of a clinical and biochemical prediction tool for assessing the risk of urinary tract infection in preverbal children with fever. The model developed accurately predicted which patients may need further testing based on clinical variables, and who may need antibiotics therapy based on urine analysis supplement.
2. In a retrospective validation population, the clinical tool developed performed better than the diagnostic assessment algorithm recommended by the American Academy of Pediatrics.
Evidence Rating Level: 3 (Fair)
Study Rundown: Evaluation of a fever in a pre-verbal infant remains a difficult clinical scenario for clinicians. Urinary tract infection (UTI) is a common cause of fever in this population, representing up to 7% of cases. Diagnostic algorithms have previously been developed and recommended by the American Academy of Pediatrics, but populations used to validate these tools have been small and many clinicians do not adhere to them. The current study sought to develop prediction models to aid clinicians in deciding who may need further work up for UTI based on clinical features, and in conjunction with quickly obtained analysis of the urine (biochemical dipstick, microscopic analysis), decisions on upfront antibiotic treatment. The developed tool performed better than the AAP recommended algorithm in the validation population. Use of the tool is also predicted to reduce the delay-to-treatment for those who would otherwise wait for urine culture results.
The main strengths of the study included the large population both for development and validation of the tool. The main limitations of the study were the single center design, which limits generalizability.
In-Depth [case-control study]: This study used a consecutive retrospective population of children aged 2 months to 2 years with a fever of 38°C who presented to Children’s Hospital of Pittsburgh between Jan. 2007 and April 2013. Patients with confirmed UTI (based on positive culture of at least 50 000 CFU/mL, along with pyuria based on WBC count or positive leukocyte esterase) and a randomly selected population without UTI were included in a nested case-control design for the development of the prediction tools. The developed algorithm used 5 models: one with only clinical features and four with clinical and biochemical/cytologic data. A secondary database was created to validate the prediction tool.
The training database used for the development of the model included 1686 children, while the validation database had 384 children. Compared to the AAP algorithm, the developed prediction tool had greater sensitivity, missing fewer cases of UTI, while also reducing unnecessary testing by 8.1%. Compared to a method of treated all those with leukocyte esterase result of ≥1+, the designed tool reduced the delay to therapy by 10.6%(95%CI, 0.9%-20.4%).
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