Establishment of a new predictive model for the recurrence of upper urinary tract stones

Abstract

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 find out the significant 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 specificity. The results showed that multiple stones (OR:1.832,95%CI:1.240–2.706), bilateral stones (OR:1.779,95%CI: 1.226–2.582), kidney stones (OR: 3.268, 95% CI: 1.638–6.518) and kidney ureteral stone (OR: 3.375, 95% CI:1.649–6.906) were high risks factors. And the stone recurrence risk was positively correlated with creatinine (OR:1.012,95%CI:1.006–1.018), urine pH (OR:1.967, 95%CI:1.343–2.883), Apo B (OR:4.189, 95%CI:1.985–8.841) and negatively correlated with serum phosphorus (OR:0.282, 95%CI:0.109–0.728). In addition, the sensitivity and specificity of the prediction model were 73.08% and 61.25%, diagnosis values were greater than any single variable. It means the model can effectively evaluate the recurrence risk of upper urinary stones, especially suitable for stone postoperative patients, to help reduce the possibility of postoperative stone recurrence.