J Cancer 2020; 11(15):4324-4331. doi:10.7150/jca.45055
Establishing a prediction model of infection during the intravesical instillation of bladder cancer: a multicenter retrospective study
1. Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
2. Department of Dermatology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
3. Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
4. Department of Urology, Xiangyang Central Hospital, Xiangyang 441021, China
#These authors contributed equally to this work.
Chen S, Yang Y, Luo Z, Deng H, Peng T, Guo Z. Establishing a prediction model of infection during the intravesical instillation of bladder cancer: a multicenter retrospective study. J Cancer 2020; 11(15):4324-4331. doi:10.7150/jca.45055. Available from http://www.jcancer.org/v11p4324.htm
Objective: To explore the independent risk factors of infection during the intravesical instillation of bladder cancer and establish a prediction model, which may reduce probability of infection for bladder cancer patients.
Material and Methods: 533 patients with newly discovered NMIBC at two hospitals from January 2017 to December 2019 were enrolled in this study. The patients were divided into “infection positive group” and “infection negative group”. The clinical data of the two groups were analyzed by logistic regression analyses. Nomogram was generated and ROC curve, calibration curve were structured to assess the accuracy of nomogram. An independent cohort included 174 patients from another hospital validated the nomogram prediction model.
Results: Of 533 patients, 185 patients had an infection. Univariate and multivariate logistic regression analyses showed diabetes mellitus, hemiplegia, patients without antibiotics and perfusion frequency (≥2 times/month) were the independent risk factors. AUC of the ROC was 0.858 (0.762-0.904). The nomogram could predict the probability of infection during the intravesical instillation of bladder tumor calibration curve showed good agreement. The external data validation gained good sensitivity and specificity, which indicated that the nomogram had great value of prediction.
Conclusions: Individualized prediction of the probability of infection may reduce the incidence of infection by argeted preventive measures.
Keywords: Bladder cancer, TURBT, Intravesical instillation, Infection, Prediction model