Evidence map›Paper›PMID 40495007›Full record

ArticleWorld journal of urology2025

Stone-free rate after RIRS: a multivariable analysis and predictive nomogram from a single-center study.

Kemal Kayar, Ridvan Kayar, Kayhan Gorkem Tuncel, Cagatay Tosun, Omer Ergin Yucebas

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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. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Kemal KayarDepartment of Urology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, Tibbiye Street. No: 23, Uskudar, Istanbul, 34668, Türkiye. kemal.kayar@hotmail.com.ORCID http://orcid.org/0000-0003-0731-9877
Ridvan KayarDepartment of Urology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, Tibbiye Street. No: 23, Uskudar, Istanbul, 34668, Türkiye.ORCID http://orcid.org/0000-0002-1765-919X
Kayhan Gorkem TuncelDepartment of Urology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, Tibbiye Street. No: 23, Uskudar, Istanbul, 34668, Türkiye.ORCID http://orcid.org/0009-0007-9619-8232
Cagatay TosunDepartment of Urology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, Tibbiye Street. No: 23, Uskudar, Istanbul, 34668, Türkiye.ORCID http://orcid.org/0000-0001-8221-8158
Omer Ergin YucebasDepartment of Urology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, Tibbiye Street. No: 23, Uskudar, Istanbul, 34668, Türkiye.ORCID http://orcid.org/0000-0002-8161-4068

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveResidual stone after Retrograde Intrarenal Surgery (RIRS) is a major challenge. This study evaluates key predictors of residual stones and develops a nomogram-based risk stratification model. MATERIALS AND

methodsA retrospective analysis of 274 patients undergoing RIRS for renal calculi (2021-2024) was conducted. Demographic, clinical and radiological variables were assessed. Multivariate logistic regression and ROC curve analysis were used to identify predictors. A nomogram was developed and validated using Python libraries, with performance assessed via concordance index (C-index) and calibration plots.

resultsStone volume > 498 mm³ (AUC: 0.819, OR: 6.34, p < 0.001), IPA ≤ 44° (OR: 7.81, p = 0.005) and multiple stony calyces (OR: 2.38, p < 0.001) were the strongest predictors of residual stones. The nomogram demonstrated excellent discrimination (C-index: 0.839) and stratified patients into four risk categories (0-150 + points), with stone-free rates ranging from > 85% (low-risk) to < 40% (high-risk). ROC analysis highlighted stone volume (AUC: 0.819) as superior to stone size (AUC: 0.793).

conclusionThe nomogram integrating stone volume, IPA and calyceal involvement provides a clinically usable tool for predicting residual stones post-RIRS. Preoperative use of this model may enhance surgical outcomes by guiding personalized treatment strategies and enables the surgeon to inform the patient more accurately about the expected outcome of the procedure.

Indexed as

Kidney CalculiNomogramsAdultAgedFemaleHumansMaleMiddle AgedMultivariate AnalysisRetrospective StudiesRisk AssessmentInfundibulopelvic angle (IPA)Predictive nomogramRetrograde intrarenal surgery (RIRS)Stone-free rate (SFR)Stone volume

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PMID40495007

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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.