Does volume matter? Incorporating estimated stone volume in a nomogram to predict ureteral stone passage

Introduction: Recent studies have shown that software-generated 3D stone volume calculations are better predictors of stone burden than measured maximal axial stone diameter. However, no studies have assessed the role of formula estimated stone volume, a more practical and cheaper alternative to software calculations, to predict spontaneous stone passage (SSP). Methods: We retrospectively included patients discharged from our emergency department on conservative treatment for ureteral stone (≤10 mm). We collected patient demographics, comorbidities, and laboratory tests. Using non-contrast computed tomography (CT) reports, stone width, length, and depth (w, l, d, respectively) were used to estimate stone volumes using the ellipsoid formula: V=π*l*w*d*0.167. Using a backward conditional regression, two models were developed incorporating either estimated stone volume or maximal axial stone diameter. A receiver operator characteristic (ROC) curve was constructed and the area under the curve (AUC) was computed and compared to the other model. Results: We included 450 patients; 243 patients (54%) had SSP and 207 patients (46%) failed SSP. The median calculated stone volume was significantly smaller among patients with SSP: 25 (14–60) mm3 vs. 113 (66–180) mm3 (p75 (OR 4.83, 95% CI 2.12–11.00), and proximal stone (OR 2.11, 95% CI 1.16–3.83). For every 1 mm3 increase in stone volume, the risk of SSP failure increased by 2.5%. The model explained 89.4% (0.864–0.923) of the variability in the outcome. This model was superior to the model including maximal axial diameter (0.881, 0.847–0.909, p=0.04). Conclusions: We present a nomogram incorporating stone volume to better predict SSP. Stone volume estimated using an ellipsoid formula can predict SSP better than maximal axial diameter.