The S.T.O.N.E. Score: a new assessment tool to predict stone free rates in ureteroscopy from pre-operative radiological features.

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.