External validation of Resorlu-Unsal stone score in predicting outcomes after retrograde intrarenal surgery. Experience from a single institution

Objective: Pre-operative assessment of renal stones is essential in selecting treatment options and achieving high success rates for retrograde intrarenal surgery (RIRS). Several nephrolithometric scoring systems have been developed using pre-operative clinical data and stone characteristics. Resorlu-Unsal stone score (RUSS) is composed of four different parameters, and each of them adds 1 point to the final score. One point is added in patients with stone size > 20 mm, lower calyceal stones and infundibulo-pelvic angle < 45 degrees, stone number > 1, and abnormal anatomy, respectively. RUSS categorizes patients into four distinct groups and aims to predict stone-free rates (SFR) after RIRS. We externally validated RUSS and evaluated its predictive accuracy. Materials and Methods: We performed a retrospective analysis of patients who underwent RIRS for renal stones between January 2020 and December 2021. Patient age, pre-operative hydronephrosis, stone size, stone density as Hounsfield Unit (HU), operative time and RUSS were investigated as potential preoperative predictive factors for stone-free status. RUSS was applied to all patients, and the nomogram was externally validated. Area under the curve (AUC) was used for clinical validity assessment. Results: The present study included a total of 79 patients. Mean patient age was 55.1 +/- 15.4 years with a mean stone size was 14.2 +/- 4.4 mm. Overall, 62/79 (78.4%) patients were stone free after the initial treatment. After applying RUSS, 36 (45.6%), 29 (36.7%), 10 (12.6%), and 4 (5.1%) patients had a score of 0, 1, 2, and 3, respectively. On multivariate logistic regression RUSS (OR = 0.220; 95%CI: 0.086-0.567; p = 0.002) was identified as the only predictor of postoperative stone-free status. Conclusions: RUSS is a user-friendly scoring system that may predict postoperative stone-free rate after RIRS with great efficacy and accuracy.