Model for predicting high-grade vesicoureteral reflux in young children presenting with febrile urinary tract infection

Klubdaeng, Anuvat; Chaiyapak, Thanaporn; Sumboonnanonda, Achra; Piyaphanee, Nuntawan

doi:10.1016/j.jpurol.2022.06.006

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…Another parameter strongly related to malformations is infection with a non‐ E. coli pathogen 10,11,21 . In our study, having an infection with an atypical pathogen increased the probability of underlying malformations by four times.…”

Section: Discussionmentioning

confidence: 48%

“…19 Different studies have identified various risk factors associated with NUM, and some heterogeneous recommendations have been made based on these criteria. 10,11,20,21 However, until now, there has yet to be a predictive model with good predictive capacity, based solely on clinical and analytical parameters, capable of stratifying patients into risk groups for individualised imaging protocols.…”

Section: Discussionmentioning

confidence: 99%

“…Patients older than 2 years with fUTIs beyond the typical age of presentation are at higher risk of recurrent UTIs, 28 raising suspicion of intrinsic predisposition, such as urinary tract abnormalities. In a study of 324 children with fUTI, 21 96 patients had VUR, and 16 (17%) were older than 24 months. Eight of these patients had high‐grade VUR.…”

Section: Discussionmentioning

confidence: 99%

“…raising suspicion of intrinsic predisposition, such as urinary tract abnormalities. In a study of 324 children with fUTI,21 96 patients had VUR, and 16 (17%) were older than 24 months. Eight of these patients had high-grade VUR.…”

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confidence: 99%

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

González‐Bertolín,

Barbas Bernardos,

Zarauza Santoveña

et al. 2024

Acta Paediatrica

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AimTo 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.MethodsWe 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.ResultsOut 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.ConclusionOur 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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Section: Discussionmentioning

confidence: 48%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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

González‐Bertolín,

Barbas Bernardos,

Zarauza Santoveña

et al. 2024

Acta Paediatrica

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Vesicoureteral Reflux

Bortnick

Nelson

2023

Urologic Clinics of North America

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Prediction model for severe vesicoureteral reflux in children with urinary tract infection and/or hydronephrosis

laleoglu,

Yildiz,

Bayram

et al. 2024

Preprint

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Background As voiding cystourethrography is invasive and carries the risks of radiation and urinary tract infection, identifying only high-grade reflux is important. We aimed to identify risk factors for severe reflux in children presenting with urinary tract infections and/or urinary tract dilatation and to develop a prediction model for severe reflux. Methods Data of the children who underwent voiding cystourethrography due to urinary tract infections and/or urinary tract dilatation were retrospectively analyzed for demographic, clinical and imaging findings. Patients with severe (grades 4–5) reflux were compared with the rest for these parameters and a prediction model was developed for severe reflux. Results The study included 1044 patients (574 female). Severe reflux was present in 86 (8.2%) patients. Non-E. coli uropathogens, hydronephrosis, UTD-P3 dilatation, multiple renal scar, and decreased renal function on DMSA scintigraphy were associated with severe reflux. The prediction model by using these variables for severe reflux with a score ranging from 0–6 and an accuracy rate of 93.4% was developed. A score of ≥ 4 had a sensitivity 48.8%, specificity 95.8%, PPV 51.2%, and NPV 95.4% for severe reflux. Patients with ≥ 4 score were 21.9 times more likely to have severe reflux. Conclusion Non-E. coli uropathogen growth, the presence of hydronephrosis and especially UTD-P3 dilatation on ultrasonography, presence of multiple scars and decreased relative function on DMSA scintigraphy were found to be independent risk factors for severe reflux. Our scoring system based on these variables appears to be effective in predicting the presence of severe VUR.

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Model for predicting high-grade vesicoureteral reflux in young children presenting with febrile urinary tract infection

Cited by 3 publications

References 27 publications

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

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

Vesicoureteral Reflux

Prediction model for severe vesicoureteral reflux in children with urinary tract infection and/or hydronephrosis

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