Utility of stone volume estimated by software algorithm in predicting success of medical expulsive therapy

Abstract

Introduction: We sought to assess the accuracy of using stone volume (SV) estimated with a software algorithm as a predictor for stone passage in a trial of medical expulsive therapy (MET). Methods: We identified patients with ureteral stones discharged from the ER on MET. Patients with infection, non-ureteral stones, or needing immediate surgical intervention were excluded. For each stone, longest dimension (LD) was recorded and SV was estimated by a computed tomography (CT)-based region growing (RG) algorithm and standard ellipsoid formula (EF). Stone passage within 30 days was assessed via electronic chart and followup phone call. Results: Fifty-one patients were included for analysis (53±16.7 years, 24% female). The mean LD was 4.85±2.02 mm. The mean SV was similar by EF and RG (0.051±0.057cm3 vs. 0.049± 0.052 cm3; p=0.28). Thirty-three (65%) patients passed their stone, while 18 (35%) did not. The mean LD for passed stones vs. failed passage was 4.1±1.7 mm vs. 6.2±1.8 mm (p=0.0002); the mean EF volume was 0.028±0.035 cm3 vs. 0.093±0.066 cm3 (p=0.00007); and the mean volume by RG was 0.028±0.027cm3 vs. 0.088±0.063 cm3 (p=0.00005). Conclusions: The clinical utility of using SV estimated by software algorithm as a predictor for success of MET has not previously been examined. We demonstrate that spontaneously passed stones had a significantly smaller volume than those requiring intervention. Further prospective studies are needed to validate these findings and establish volume thresholds for probability of stone passage.