Wilson R Molina1, F J Kim1, Joshua Spendlove2, Alexandre S Pompeo3, Stefan Sillau4, David E Sehrt5. 1. Department of Urology, Denver Health Medical Center, Denver, CO, USA and Department of Urology, University of Colorado, Aurora, CO, USA. 2. Department of Urology, University of Colorado, Aurora, CO, USA. 3. Department of Urology, Denver Health Medical Center, Denver, CO, USA and Department of Urology (ASP), ABC Medical School, Sao Paulo, Brazil. 4. Department of Statistics, University of Colorado, Aurora, CO, USA. 5. Department of Urology, Denver Health Medical Center, Denver, CO, USA.
Abstract
OBJECTIVE: To develop a user friendly system (S.T.O.N.E. Score) to quantify and describe stone characteristics provided by computed axial tomography scan to predict ureteroscopy outcomes and to evaluate the characteristics that are thought to affect stone free rates. MATERIALS AND METHODS: The S.T.O.N.E. score consists of 5 stone characteristics: (S) ize, (T)opography (location of stone), (O)bstruction, (N)umber of stones present, and (E)valuation of Hounsfield Units. Each component is scored on a 1-3 point scale. The S.T.O.N.E. Score was applied to 200 rigid and flexible ureteroscopies performed at our institution. A logistic model was applied to evaluate our data for stone free rates (SFR). RESULTS: SFR were found to be correlated to S.T.O.N.E. Score. As S.T.O.N.E. Score increased, the SFR decreased with a logical regression trend (p < 0.001). The logistic model found was SFR=1/(1+e^(-z)), where z=7.02-0.57•Score with an area under the curve of 0.764. A S.T.O.N.E. Score ≤ 9 points obtains stone free rates > 90% and typically falls off by 10% per point thereafter. CONCLUSIONS: The S.T.O.N.E. Score is a novel assessment tool to predict SFR in patients who require URS for the surgical therapy of ureteral and renal stone disease. The features of S.T.O.N.E. are relevant in predicting SFR with URS. Size, location, and degree of hydronephrosis were statistically significant factors in multivariate analysis. The S.T.O.N.E. Score establishes the framework for future analysis of the treatment of urolithiasis.
OBJECTIVE: To develop a user friendly system (S.T.O.N.E. Score) to quantify and describe stone characteristics provided by computed axial tomography scan to predict ureteroscopy outcomes and to evaluate the characteristics that are thought to affect stone free rates. MATERIALS AND METHODS: The S.T.O.N.E. score consists of 5 stone characteristics: (S) ize, (T)opography (location of stone), (O)bstruction, (N)umber of stones present, and (E)valuation of Hounsfield Units. Each component is scored on a 1-3 point scale. The S.T.O.N.E. Score was applied to 200 rigid and flexible ureteroscopies performed at our institution. A logistic model was applied to evaluate our data for stone free rates (SFR). RESULTS: SFR were found to be correlated to S.T.O.N.E. Score. As S.T.O.N.E. Score increased, the SFR decreased with a logical regression trend (p < 0.001). The logistic model found was SFR=1/(1+e^(-z)), where z=7.02-0.57•Score with an area under the curve of 0.764. A S.T.O.N.E. Score ≤ 9 points obtains stone free rates > 90% and typically falls off by 10% per point thereafter. CONCLUSIONS: The S.T.O.N.E. Score is a novel assessment tool to predict SFR in patients who require URS for the surgical therapy of ureteral and renal stone disease. The features of S.T.O.N.E. are relevant in predicting SFR with URS. Size, location, and degree of hydronephrosis were statistically significant factors in multivariate analysis. The S.T.O.N.E. Score establishes the framework for future analysis of the treatment of urolithiasis.
Authors: Boyd R Viers; Matthew K Tollefson; David E Patterson; Matthew T Gettman; Amy E Krambeck Journal: World J Clin Cases Date: 2014-11-16 Impact factor: 1.337
Authors: Sinan Levent Kirecci; Musab Ilgi; Cumhur Yesildal; Abdullah Hizir Yavuzsan; Ahmet Tevfik Albayrak; Kemal Sarica Journal: Urol Ann Date: 2021-03-04