Evidence map›Paper›PMID 41264020›Full record

ArticleWorld journal of urology2025

Efficacy and safety of ureteroscopy in children with lower pole renal stones : a machine learning predictive model from the EAU section of endourology.

Carlotta Nedbal, Vineet Gauhar, Shilpa Gite, Het Sevalia, Ratan Maurya, Prisha Jaiswal, Khushi Kashyap, Andrea Gregori, Francesco Antomarchi, Frédéric Panthier and 8 more

Abstract readMulticenter Study
In one paragraph

Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Carlotta NedbalPolytechnic University Le Marche, Ancona, Italy. carlottanedbal@gmail.com.ORCID http://orcid.org/0000-0003-2031-347X
Vineet GauharEndourology Section, European Association of Urology, Arnhem, The Netherlands.
Shilpa GiteSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.
Het SevaliaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.
Ratan MauryaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.
Prisha JaiswalSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.
Khushi KashyapSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.
Andrea GregoriIRCCS San Gerardo dei Tintori, Monza, Italy.
Francesco AntomarchiIRCCS San Gerardo dei Tintori, Monza, Italy.
Frédéric PanthierEndourology Section, European Association of Urology, Arnhem, The Netherlands.
Yiloren TanidirDepartment of Urology, Marmara University School of Medicine, Istanbul, Turkey.
Abhishek SinghMuljibhai Patel Urological Hospital, Nadiad, Gujarat, India.
Boyke SoebhaliDepartment of Urology, Abdul Wahab Sjahranie Hospital Medical Faculty, Muliawarman University, Samarinda, Indonesia.
Hsiang Ying LeeDepartment of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
Steffi Kar Kei YuenDepartment of Surgery, SH Ho Urology Centre, The Chinese University of Hong Kong, Shatin, Hong Kong.
Ee Jean LimDepartment of Urology, Singapore General Hospital, Singapore, Singapore.
Nitesh NaikDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 57614, Karnataka, India.
Bhaskar Kumar SomaniEndourology Section, European Association of Urology, Arnhem, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe rising incidence of kidney stone disease in children presents growing clinical challenges, particularly in managing lower pole (LP) calculi, which are anatomically difficult to treat. Flexible ureteroscopy with laser lithotripsy (fURSL) has emerged as a preferred minimally invasive treatment. However, surgical outcomes remain variable, especially in the paediatric LP stone cohort. This study aimed to apply machine learning (ML) techniques to predict surgical outcomes based on preoperative characteristics and identify key predictors of incomplete stone clearance. MATERIALS AND

methodsA retrospective analysis was conducted on paediatric patients (≤ 16 years) who underwent fURSL between January 2017 and December 2021 across eight tertiary centres. From a multicentre database of 280 patients, 91 with isolated LP stones were selected. Preoperative, intraoperative, and postoperative variables were analysed. Fifteen ML models-including ensemble algorithms and a multitask neural network-were developed to predict LP stone presence and postoperative outcomes. Model performance was evaluated using accuracy, precision, recall, F1-score, and SHAP (SHapley Additive exPlanations) values for interpretability.

resultsLP stones were present in 32.5% of cases and were associated with older age, solitary stones, and higher stone burden. Random Forest outperformed all other models (validation accuracy: 80.95%; F1-score: 76.67%), followed by Gradient Boosting. SHAP analysis identified stone number, total stone burden, age, and operative time as top predictors. LP stones were associated with a higher rate of residual fragments (RF) and lower need for preoperative stenting or ureteral access sheath use. Infectious and bleeding complications were less frequent in the LP group.

conclusionfURSL is safe and effective in children with LP stones, though incomplete stone clearance remains a challenge. ML models demonstrated strong predictive performance and could support preoperative risk stratification. Further external validation and prospective studies are warranted to refine predictive tools for clinical use.

Indexed as

Kidney CalculiLithotripsy, LaserMachine LearningUreteroscopyAdolescentChildChild, PreschoolFemaleHumansMaleRetrospective StudiesTreatment OutcomeLower pole stonesMachine learningPaediatric urologyPredictive toolsStone free rates.Urolithiasis

Identifiers

PMID41264020
PMCPMC12634796

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.