Risk Factors of Catheter-Associated Urinary Tract Infections Following Radical Hysterectomy for Cervical Cancer: A Propensity Score Matching-Based Study.

PURPOSE

This study aims to examine the risk factors for catheter-associated urinary tract infection (CAUTI) following radical hysterectomy for cervical cancer (CC). Furthermore, the study seeks to develop a visual model that can effectively assist physicians in improving their proficiency in diagnosing, treating, and preventing CAUTIs.

PATIENTS AND METHODS

48 subjects who developed CAUTI postoperatively were assigned to the infection group. There were 443 cases who did not develop CAUTI, and a 1:1 propensity score matching (PSM) method was employed to match 48 cases for the non-infection group. Univariate logistic and multivariate stepwise regression analyses were used to analyze the risk factors for CAUTI following radical hysterectomy for CC. Subsequently, a nomogram-based model was developed, and its effectiveness was comprehensively assessed.

RESULTS

The incidence rate of CAUTI in 491 patients who underwent radical hysterectomy for CC was 9.76% (48/491). Multivariate stepwise regression analysis revealed that the duration of urinary catheterization, urinary leukocyte esterase, and positive urine culture were the independent risk factors for CAUTI after radical hysterectomy for CC (all β > 0, < 0.05). A nomogram model incorporating these independent risk factors was constructed, and receiver operating characteristic (ROC) and decision curve analysis (DCA) curves were generated. The ROC curve exhibited an area under the curve value of 0.9035, 95% CI of 0.8352-0.9718, specificity of 0.8214, sensitivity of 0.8571, accuracy of 0.8429, positive predictive value of 0.8780, and negative predictive value of 0.7931.

CONCLUSION

The duration of urinary catheterization, urinary leukocyte esterase, and positive urine culture are independent risk factors for CAUTI after radical hysterectomy for CC. This nomogram-based model exhibits numerous advantages, including simplicity, user-friendliness, high diagnostic accuracy, and significant clinical value, which can provide assistance in early clinical diagnosis decision-making.