[Skip to Content]
[Skip to Content Landing]
Original Investigation
June 2018

Development and Validation of a Calculator for Estimating the Probability of Urinary Tract Infection in Young Febrile Children

Author Affiliations
  • 1Division of General Academic Pediatrics, Children’s Hospital of Pittsburgh of University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
  • 2Medical student, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • 3Institute for Clinical Research Education, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
JAMA Pediatr. 2018;172(6):550-556. doi:10.1001/jamapediatrics.2018.0217
Key Points

Question  In febrile children younger than 2 years, what combination of clinical and laboratory findings best predicts the risk of urinary tract infection?

Findings  This nested case-control study of 2070 children aged 2 to 23 months with a documented temperature of 38°C or higher tested the accuracy of UTICalc, an algorithm that uses clinical and laboratory findings to estimate the probability of urinary tract infection. Compared with the American Academy of Pediatrics algorithm, UTICalc reduced testing by 8.1% and decreased the number of urinary tract infections that were missed.

Meaning  The UTICalc calculator can be used to guide testing and treatment in children with suspected urinary tract infection.

Abstract

Importance  Accurately estimating the probability of urinary tract infection (UTI) in febrile preverbal children is necessary to appropriately target testing and treatment.

Objective  To develop and test a calculator (UTICalc) that can first estimate the probability of UTI based on clinical variables and then update that probability based on laboratory results.

Design, Setting, and Participants  Review of electronic medical records of febrile children aged 2 to 23 months who were brought to the emergency department of Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania. An independent training database comprising 1686 patients brought to the emergency department between January 1, 2007, and April 30, 2013, and a validation database of 384 patients were created. Five multivariable logistic regression models for predicting risk of UTI were trained and tested. The clinical model included only clinical variables; the remaining models incorporated laboratory results. Data analysis was performed between June 18, 2013, and January 12, 2018.

Exposures  Documented temperature of 38°C or higher in children aged 2 months to less than 2 years.

Main Outcomes and Measures  With the use of culture-confirmed UTI as the main outcome, cutoffs for high and low UTI risk were identified for each model. The resultant models were incorporated into a calculation tool, UTICalc, which was used to evaluate medical records.

Results  A total of 2070 children were included in the study. The training database comprised 1686 children, of whom 1216 (72.1%) were female and 1167 (69.2%) white. The validation database comprised 384 children, of whom 291 (75.8%) were female and 200 (52.1%) white. Compared with the American Academy of Pediatrics algorithm, the clinical model in UTICalc reduced testing by 8.1% (95% CI, 4.2%-12.0%) and decreased the number of UTIs that were missed from 3 cases to none. Compared with empirically treating all children with a leukocyte esterase test result of 1+ or higher, the dipstick model in UTICalc would have reduced the number of treatment delays by 10.6% (95% CI, 0.9%-20.4%).

Conclusions and Relevance  UTICalc estimates the probability of UTI by evaluating the risk factors present in the individual child. As a result, testing and treatment can be tailored, thereby improving outcomes for children with UTI.

×