Establishment of predictive model for infection after transurethral resection of bladder tumor by infusion therapy

HU Boyong; HAN Ming; YAO Bing; QIU Jin

doi:10.3969/j.issn.1009-0460.2019.01.009

Abstract：Objective To explore the influencing factors of infection after transurethral resection of bladder tumor (TURBT by infusion therapy and establish a predictive model. Methods A total of 213 patients who underwent regular bladder perfusion following TURBT from March 2014 to March 2017 were enrolled. Patients were divided into infection group and non-infection group according to whether infection occured or not. The clinical data of the two groups were analyzed by univariate and multivariate Logistic regression analysis, the receiver operating characteristic curve (ROC) curves were generated for multivariable Logistic regression analysis, and the independent risk factors were analyzed as drawing nomogram. Results Of 213 patients, a total of 47 patients had infection (22. 1％). The univariate Logistic regression analysis showed that sex, age, combined diabetes mellitus, hemiplegia, antibiotics treatment and perfusion frequency were related to the infection after perfusion therapy for bladder tumor (P ＜ 0. 05). Multivariate Logistic regression analysis showed that diabetes mellitus, hemiplegia, antibiotics treatment and perfusion frequency were independent risk factors for the occurrence of infection of postoperative perfusion for bladder tumor. The area under the curve (AUC) of the ROC was 0. 858 (0. 762-0. 904), and the nomogram can predict the probability of infection after bladder perfusion in patients with single bladder tumor by independent risk factors, with a sensitivity of 81. 2％ and a specificity of 79. 8％. Conclusion The occurrence of infection in bladder tumor perfusion comes from a variety of factors. Individualized prediction of the probability of infection may reduce the incidence of infection by targeted preventive measures.