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

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 stu...

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Detalhes bibliográficos
Autores: González-Bertolín, I. (Isabel)|||/items/d08c9d12-006c-414e-a45c-e640f82e04fe, Barbas-Bernardos, G. (Guillermo)|||/items/020a657b-3ca4-4560-a514-e9e10784cbb4, Zarauza-Santoveña, A. (Alejandro)|||/items/642ed7ec-8b09-4b24-9c07-e99487366118, García-Suárez, L. (Leire)|||/items/497d21b3-de62-45a5-8405-23b2fec16bc1, López-López, R. (Rosario)|||/items/eb438a87-33f9-4281-95fc-9b64142d1ff6, Plata Gallardo, M. (Marta)|||/items/ae8c2a67-c7b1-4601-9a45-72e3b0a17e72, Miguel-Cáceres, C. (Cristina) de|||/items/8199b401-f091-4dc3-9716-24f9736b420b, Calvo, C. (Cristina)|||/items/84d27b58-ae36-4ab9-9dac-59525c1122cd
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/69977
Acesso em linha:https://hdl.handle.net/10171/69977
Access Level:acceso abierto
Palavra-chave:Febrile urinary tract infection
Logistic model
Urinary tract malformations
Descrição
Resumo: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.

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