Statistical analysis
Categorical data were presented with number and percentage of rows. Only ESWL, preoperative stent requirement, and complication rates were presented as percentages of column for the convenience of comparison for the reader. Continuous data were evaluated using the Kolmogorov-Smirnov test to verify the normality of distribution of variables. Normally distributed data were expressed with mean + standard deviation (SD), and non-normally distributed data with median and percentile (25-75th) values. The independent-samples t-test was used to compare two independent normally distributed data, while the Mann-Whitney U test was conducted for the comparison of data without normal distribution. In the comparison of categorical variables, Pearson’s or Yate’s chi-square test was used as appropriate. The relationship of stone size and stone surface area with SFS was evaluated with the multivariate logistic regression analysis, and stone surface area was determined as an independent predictive factor [odds ratio (OR) = 1.004, p = 0.025] (Table 1). Therefore, the measurement of surface area, which is used in both computed tomography and kidney, ureter, bladder radiography in clinical practice, was undertaken to predict stone volume. Possible predictive variables associated with SFS were evaluated with the multivariate logistic regression analysis, and the Backward elimination (Wald) method was used to construct a model. The exclusion criterion for the model was set at p < 0.1. A new nomogram including stone surface area was created using the regression coefficients of independent predictive variables. The predictive ability of the nomogram was evaluated with the receiver operating characteristic (ROC) analysis. Then, the T.O.HO., STONE and modified T.O.HO. scores were calculated for each patient. The ability of the scores to predict SFS was analyzed using the ROC analysis, and sensitivity and specificity values were calculated by determining the cut-off value for each scoring. A p value of <0.05 was considered statistically significant. SPSS software (version 23.0; IBM Corporation, Armonk, NY, USA) was used for statistical analyses and the R-project statistical software and “rms” package for the construction of the nomogram.