Can CT-Based Stone Impaction Markers Augment the Predictive Ability of Spontaneous Stone Passage?

Introduction: A number of clinical and radiological predictors of either stone impaction or ureteral stone passage (SP) have been proposed. We aimed at identifying the key predictors of successful SP by using readily available CT-based tools/measurements. Methods: Patients presenting to the emergency department from February 2017 to February 2018 with an acute unilateral ureteral stone confirmed on non-contrast CT and managed conservatively were followed for SP. Patients with renal impairment, sepsis or requiring emergent intervention were excluded. Patients were followed at 1 month to confirm SP (stone collection/repeat imaging) or failure of passage. The CT variables analyzed included: Stone factors [location, size, volume, HU density (HUD)], impaction factors [ureteral HUD above and below the stone, maximal ureteral wall thickness (UWT) at the stone site, contralateral UWT, and ureteral diameter above and below the stone]. Binary logistic regression analysis was performed to identify predictors of SP. Results: Forty-nine patients met study inclusion criteria, of whom 32 (65.3%) passed the stone without further intervention. Patients with successful passage were more likely to have smaller, lower volume and less dense stones located in the distal ureter (p < 0.01). Lower ureteral HUD below the stone, lower maximal UWT, and lower ureteral diameter above the stone were associated with successful passage (p < 0.01). On multivariable logistic regression analysis, only maximal UWT at the stone site was a significant independent predictor of SP outcome (p = 0.01). Youden's criterion identified 2.3 mm as the optimal UWT cut-off point, which will accurately predict SP with 82.4% sensitivity and 87.5% specificity. Conclusions: Maximal UWT at the stone site was the most significant predictor of successful passage in acute unilateral ureteral stones, with an optimal cut-off point of 2.3 mm. Further prospective studies are needed to accurately predict spontaneous SP.

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

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 emergency department 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±0.027 cm3 vs. 0.088±0.063 cm3 (p=0.00005). CONCLUSIONS: The clinical utility of SV estimated by software algorithm as a predictor for success of MET has not previously been examined. We demonstrated 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.