Chen Song, Yang Yun, Luo Ziyi, Deng Haiqing, Peng Tiancheng, Guo Zhongqiang
Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
Department of Dermatology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China.
J Cancer. 2020 Apr 27;11(15):4324-4331. doi: 10.7150/jca.45055. eCollection 2020.
: 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. : 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. : 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. : Individualized prediction of the probability of infection may reduce the incidence of infection by argeted preventive measures.
探讨膀胱癌膀胱灌注期间感染的独立危险因素并建立预测模型,以降低膀胱癌患者的感染概率。2017年1月至2019年12月,两所医院新发现的533例非肌层浸润性膀胱癌患者纳入本研究。将患者分为“感染阳性组”和“感染阴性组”。采用logistic回归分析两组的临床资料。绘制列线图,并构建ROC曲线、校准曲线以评估列线图的准确性。另一家医院的174例患者组成独立队列对列线图预测模型进行验证。533例患者中,185例发生感染。单因素和多因素logistic回归分析显示,糖尿病、偏瘫、未使用抗生素以及灌注频率(≥2次/月)是独立危险因素。ROC曲线的AUC为0.858(0.762 - 0.904)。列线图可预测膀胱肿瘤灌注期间的感染概率,校准曲线显示一致性良好。外部数据验证获得了良好的敏感性和特异性,表明列线图具有较大的预测价值。对感染概率进行个体化预测可通过针对性的预防措施降低感染发生率。