Cervical cancer (CC) patients receiving indwelling catheterization after radical hysterectomy (RH) are vulnerable to urinary tract infection (UTI). However, no model or method is available to predict the risk of UTIs. Therefore, our aim was to develop and verify a risk model to predict UTI for patients receiving indwelling catheterization after radical cervical cancer surgery (ICa-RCCS).
We first collected clinical information of 380 patients receiving ICa-RCCS from January 2020 to December 2021 as a training cohort to develop the risk nomogram. UTI was then evaluated using 19 UTI predictor factors. The least absolute shrinkage and selection operator (LASSO) method was utilized for the extraction characteristics. Multivariable logistic regression analysis was then conducted to create the risk model for UTI prediction. The consistency coefficient and calibration curve were utilized to assess the model’s fit accuracy. We performed bootstrapping with 1000 random samples for internal validation of the model, and decision curve analysis (DCA) for clinical application.
Predictors in the risk nomogram included indwelling catheterization duration, whether it is secondary indwelling catheterization, history of UTIs, age, and history of chemotherapy before surgery. The risk nomogram presented good discrimination and calibration (C-index: 0.810, 95% CI: 0.759-0.861). During interval validation, the model reached a high C-index up to 0.7930. DCA revealed the clinical utility of predictive model for UTI. Clinical benefit was initiated at the decision threshold≥3%.
We developed a novel UTI nomogram incorporating the age, history of chemotherapy before surgery, indwelling catheterization duration, whether it is secondary indwelling catheterization, and history of UTI to predict UTI risk for patients receiving ICa-RCCS.
B: 3a.
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