Establishing a prediction model of infection during the intravesical instillation of bladder cancer: a multicenter retrospective study

Chen, Song; Yang, Yun; Luo, Ziyi; Deng, Haiqing; Peng, Tiancheng; Guo, Zhongqiang

doi:10.7150/jca.45055

International Journal of Biological Sciences

International Journal of Medical Sciences

Theranostics

Journal of Genomics

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J Cancer 2020; 11(15):4324-4331. doi:10.7150/jca.45055 This issue Cite

Research Paper

Establishing a prediction model of infection during the intravesical instillation of bladder cancer: a multicenter retrospective study

Song Chen^1#, Yun Yang^2#, Ziyi Luo³, Haiqing Deng⁴, Tiancheng Peng¹, Zhongqiang Guo^1✉

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.

Citation:

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. https://www.jcancer.org/v11p4324.htm

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Abstract

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

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APA

Chen, S., Yang, Y., Luo, Z., Deng, H., Peng, T., Guo, Z. (2020). Establishing a prediction model of infection during the intravesical instillation of bladder cancer: a multicenter retrospective study. Journal of Cancer, 11(15), 4324-4331. https://doi.org/10.7150/jca.45055.

ACS

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.

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CSE

Chen S, Yang Y, Luo Z, Deng H, Peng T, Guo Z. 2020. Establishing a prediction model of infection during the intravesical instillation of bladder cancer: a multicenter retrospective study. J Cancer. 11(15):4324-4331.

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