The purpose of this study is to construct a new prediction model to evaluate the recurrence risk of upper urinary tract stones in patients. We retrospectively reviewed the clinical data of 657 patients with upper urinary tract stones and divided them into stone recurrence group and non-recurrence group. Blood routine, urine routine, biochemical and urological CT examinations were searched from the electronic medical record, relevant clinical data were collected, including age, BMI, stones number and location, hyperglycemia, hypertension, and relevant blood and urine parameters. Then, independent sample t-test, Wilcoxon rank sum test, and Chi-square test were used to preliminarily analyze the data of two groups, and then LASSO and Logistic regression analysis were used to nd out the signi cant difference indicators. Finally, R software was used to draw a nomogram to construct the model, and ROC curve was drawn to evaluate the sensitivity and speci city. The results showed that multiple stones