Can ct-based stone impaction markers augment the predictive ability of spontaneous stone passage?

Abstract

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.