Development and Validation of a Calculator for Estimating the Probability of Urinary Tract Infection in Young Febrile Children
Shaikh N, Hoberman A, Hum SW, Alberty A, Muniz G, Kurs-Lasky M, Landsittel D, Shope T. Development and Validation of a Calculator for Estimating the Probability of Urinary Tract Infection in Young Febrile Children. JAMA Pediatr. 2018 Jun 1;172(6):550-556. doi: 10.1001/jamapediatrics.2018.0217. PMID: 29710324. PMCID: PMC6137527.
Accurately estimating the probability of urinary tract infection (UTI) in febrile preverbal children is necessary to appropriately target testing and treatment.
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.
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.
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.