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Objective <p>Residual 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.</p> Materials and methods <p>A 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.</p> Results <p>Stone volume &gt; 498&#xa0;mm³ (AUC: 0.819, OR: 6.34, <i>p</i> &lt; 0.001), IPA ≤ 44° (OR: 7.81, <i>p</i> = 0.005) and multiple stony calyces (OR: 2.38, <i>p</i> &lt; 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 &gt; 85% (low-risk) to &lt; 40% (high-risk). ROC analysis highlighted stone volume (AUC: 0.819) as superior to stone size (AUC: 0.793).</p> Conclusion <p>The 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年6月10日

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

摘要

Objective

Residual 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 methods

A 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.

Results

Stone 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).

Conclusion

The 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.