Influencing Factors of Urinary Tract Stones Complicated by Urinary Tract Infections and the Construction of a Column Chart Prediction Model

Objective: To analyze the influencing factors of urinary tract stones complicated by urinary tract infections and construct a column chart prediction model. Patients and Methods: From July 2020 to October 2023, 345 patients with urinary tract stones admitted to our hospital were collected as the training set, they were separated into an infection group of 51 cases and a non-infection group of 294 cases on the basis of the presence or absence of concurrent urinary tract infections; 192 patients with urinary tract stones were used as the testing set and were divided into an infection group of 26 cases and a non-infection group of 166 cases on the basis of the presence or absence of concurrent urinary tract infections. Data such as gender, age, and procalcitonin (PCT) were recorded. Multi-variable logistic regression analysis was applied to screen predictive factors, R4.0.2 software was applied to construct a column chart model, the calibration curve and Receiver Operating Characteristic (ROC) curve were applied to evaluate the discrimination and calibration of the column chart model; decision curve analysis curve was applied to evaluate the predictive performance of column chart models. Results: The proportions of female, diabetes mellitus, indwelling time of urinary catheter ≥7 days, the PCT, and urine pH in the infected group were greater than those in the non-infected group (p < 0.05). Female, diabetes mellitus, catheter retention time ≥7 days, high PCT, and high urine pH were independent risk factors for urinary calculi complicated with urinary tract infection (p < 0.05). Training set: C-index was 0.913, Area Under Curve (AUC) was 0.943 [95% Confidence Interval (CI) = 0.912-0.973], sensitivity was 86.36%, and specificity was 89.81%, testing set: C-index was 0.905, AUC was 0.959 (95% CI = 0.928-0.989), sensitivity was 84.65%, and specificity was 95.84%, indicating good discriminability of the line graph model; Hosmer-Lemeshow test showed χ² = 2.843, 2.894, p = 0.944, 0.941, the calibration curve approached the ideal curve, and the line graph model had good calibration. When the risk threshold for urinary tract stones complicated by urinary tract infections was between 0.08 and 0.86, this column chart model provided clinical net benefits. Conclusion: The column chart prediction model for urinary tract stones complicated by urinary tract infections constructed in this study has high predictive efficiency and clinical practical value, and can provide reference for medical staff.