The objective was to develop a prediction model for urinary tract infection (UTI) after pelvic surgery.

We utilized data from three tertiary care centers of women undergoing pelvic surgery. The primary outcome was a UTI within 8 weeks of surgery. Additional variables collected included procedural data, severity of prolapse, use of mesh, anti-incontinence surgery, EBL, diabetes, steroid use, estrogen use, postoperative catheter use, PVR, history of recurrent UTI, operative time, comorbidities, and postoperative morbidity including venous thromboembolism, surgical site infection. Two datasets were used for internal validation, whereas a third dataset was used for external validation. Algorithms that tested included the following: multivariable logistic regression, decision trees (DTs), naive Bayes (NB), random forest (RF), gradient boosting (GB), and multilayer perceptron (MP).

For the training dataset, containing both University of British Columbia and Mayo Clinic Rochester data, there were 1,657 patients, with 172 (10.4%) UTIs; whereas for the University of Calgary external validation data, there were a total of 392 patients with a UTI rate of 16.1% (n = 63). All models performed well; however, the GB, DT, and RF models all had an area under the curve (AUC) > 0.97. With external validation the model retained high discriminatory ability, DT: AUC = 0.88, RF: AUC = 0.88, and GB: AUC = 0.90.

A model with high discriminatory ability can predict UTI within 8 weeks of pelvic surgery. Future studies should focus on prospective validation and application of randomized trial models to test the utility of this model in the prevention of postoperative UTI.