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Does volume matter? Incorporating estimated stone volume in a nomogram to predict ureteral stone passage
Nassib Abou Heidar1, Muhieddine Labban1, David-Dan Nguyen2
1Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon.
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 less expensive 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 (p<0.001). After adjusting for covariates, predictors of retained stone included: neutrophil to lymphocyte ratio (NLR) ≥3.14 (odds ratio [OR] 6, 95 % confidence interval [CI] 3.49-10.33), leukocyte esterase (LE) >75 (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.
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