AUTHOR=Lu Xun , Jiang Hua , Wang Dong , Wang Yiduo , Chen Qi , Chen Shuqiu , Chen Ming TITLE=Early Warning Models to Predict the 90-Day Urinary Tract Infection Risk After Radical Cystectomy and Urinary Diversion for Patients With Bladder Cancer JOURNAL=Frontiers in Surgery VOLUME=8 YEAR=2022 URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2021.782029 DOI=10.3389/fsurg.2021.782029 ISSN=2296-875X ABSTRACT=Purpose

To develop and validate a nomogram of the 90-day urinary tract infection (UTI) risk for patients with bladder cancer undergoing radical cystectomy (RC) and urinary diversion.

Patients and Methods

The predictive nomogram was based on a retrospective study on the consecutive patients who underwent RC and urinary diversion for bladder cancer between January 2014 and March 2021. The incidence and microbiology of UTI were reported. The univariate and multivariate logistic analyses were conducted to determine independent risk factors associated with UTI. The predictive accuracy and discriminatory ability of the established nomogram were evaluated by the concordance index (C-index) and decision curve analysis (DCA). The performance of the model was validated internally.

Results

A total of 220 patients were included and the incidence of UTI within 90 days was 27.3%. The most commonly identified pathogens were Enterococcus (42.0%), Escherichia coli (21.70%), and Candida (13.0%). Urinary diversion type, Charlson comorbidities index (CCI), stricture, and prognostic nutritional index (PNI) were included in the nomogram. The C-index of the nomogram for predicting UTI was 0.858 (95% CI: 0.593–0.953). In the validation cohort, the nomogram also showed high-predictive accuracy. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) index indicated that PNI led to improvement in predictive ability.

Conclusion

The proposed early warning model shows great accuracy in predicting the incidence of 90-day UTI after RC and urinary diversion in patients with bladder cancer.