Nassib Abou Heidar1, Muhieddine Labban1, David-Dan Nguyen2, Adnan El-Achkar1, Mazen Mansour1, Naeem Bhojani3, Rami Nasr1. 1. Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon. 2. Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada. 3. Division of Urology, Department of Surgery, Centre hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Abstract
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.
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.
Authors: Shadpour Demehri; Michael L Steigner; Aaron D Sodickson; E Andres Houseman; Frank J Rybicki; Stuart G Silverman Journal: AJR Am J Roentgenol Date: 2012-03 Impact factor: 3.959
Authors: J Sáenz Medina; R O Alarcón Parra; E Redondo González; L Llanes González; L Crespo Martínez; L Fernández Montarroso; M Durán Poveda; A Páez Borda Journal: Actas Urol Esp Date: 2010-11 Impact factor: 0.994
Authors: Michael Ordon; Sero Andonian; Brian Blew; Trevor Schuler; Ben Chew; Kenneth T Pace Journal: Can Urol Assoc J Date: 2015-12-14 Impact factor: 1.862
Authors: Taimur T Shah; Chuanyu Gao; Max Peters; Todd Manning; Sophia Cashman; Arjun Nambiar; Marcus Cumberbatch; Ben Lamb; Anthony Peacock; Marieke J Van Son; Peter S N van Rossum; Robert Pickard; Paul Erotocritou; Daron Smith; Veeru Kasivisvanathan Journal: BJU Int Date: 2019-05-14 Impact factor: 5.588