Prediction of Retrograde Intrarenal Surgery Outcomes in Patients With Horseshoe Kidneys Using Three Different Scoring Systems: A Critical Single-center Experience

Abstract

OBJECTIVE To evaluate the predictive value of three different scoring systems S.T.O.N.E., Clinical Research Office of the Endourological Society (CROES), and R.I.R.S. in determining stone-free (SF) rates following retrograde intrarenal surgery (RIRS) for kidney stones in patients with horseshoe kidneys (HSK). METHODS A retrospective analysis was conducted on 56 patients with HSK who underwent RIRS for kidney stones <= 2 cm between 2015 and 2022. Stone characteristics, procedural details, and outcomes were analyzed. Scoring systems were evaluated for their predictive accuracy using univariate and multivariate logistic regression analyzes, as well as receiver operating characteristic curve analysis. RESULTS Mean age of the participants was 47.37 +/- 14.42 years and mean stone size was 15.41 +/- 2.9 mm. The overall SF rate after a single RIRS session was 76.7% (n = 43). In multivariate analysis, lower calyceal location (P = .015), stone size (P = .003), S.T.O.N.E. score (P = .049), and CROES score (P = .032) were significantly associated with SF status. Receiver operating characteristic analysis revealed that the CROES score had the highest predictive accuracy (AUC = 0.785), followed by the S.T.O.N.E. score (AUC = 0.645), while the R.I.R.S. score showed poor predictive ability (AUC = 0.434). Complications occurred in six patients; 4 grade 1, 1 grade 2, and 1 grade 3 according to Clavien-Dindo classification. CONCLUSION Among the scoring systems evaluated, the CROES nomogram demonstrated superior predictive accuracy for SF status after RIRS in patients with HSK, followed by the S.T.O.N.E. score. Factors such as stone size, lower calyceal location, and comprehensive scoring systems are crucial in predicting treatment success, underscoring the need for tailored approaches in managing kidney stones in HSK patients. UROLOGY 204: 45-50, 2025. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.