NUM-score: A clinical-analytical model for personalised imaging after urinary tract infections

Abstract

Aim: To identify predictive variables and construct a predictive model along with a decision algorithm to identify nephrourological malformations (NUM) in children with febrile urinary tract infections (fUTI), enhancing the efficiency of imaging diagnostics.

Methods: We performed a retrospective study of patients aged <16 years with fUTI at the Emergency Department with subsequent microbiological confirmation between 2014 and 2020. The follow-up period was at least 2 years. Patients were categorised into two groups: 'NUM' with previously known nephrourological anomalies or those diagnosed during the follow-up and 'Non-NUM' group.

Results: Out of 836 eligible patients, 26.8% had underlying NUMs. The study identified six key risk factors: recurrent UTIs, non-Escherichia coli infection, moderate acute kidney injury, procalcitonin levels >2 μg/L, age <3 months at the first UTI and fUTIs beyond 24 months. These risk factors were used to develop a predictive model with an 80.7% accuracy rate and elaborate a NUM-score classifying patients into low, moderate and high-risk groups, with a 10%, 35% and 93% prevalence of NUM. We propose an algorithm for approaching imaging tests following a fUTI.

Conclusion: Our predictive score may help physicians decide about imaging tests. However, prospective validation of the model will be necessary before its application in daily clinical practice.

Keywords: febrile urinary tract infection; logistic model; urinary tract malformations.