Development and validation of a risk prediction model for postoperative catheter-associated urinary tract infection in patients with gynecologic malignancies
10.3760/cma.j.cn115682-20210718-03178
- VernacularTitle:妇科恶性肿瘤患者术后导尿管相关尿路感染风险预测模型的建立及验证
- Author:
Liyao XIA
1
;
Chunlan WANG
;
Shuying LIU
Author Information
1. 中国科学院大学附属肿瘤医院（浙江省肿瘤医院）妇瘤科，中国科学院基础医学与肿瘤研究所，杭州 310022
- Keywords:
Gynecologic neoplasms;
Catheter-related urinary tract infection;
Risk prediction
- From:
Chinese Journal of Modern Nursing
2022;28(6):809-813
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a risk prediction model for postoperative catheter-associated urinary tract infection (CAUTI) in patients with gynecologic malignancies, so as to provide a reference for the prevention of CAUTI.Methods:From January to June 2020, convenience sampling was adopted to select postoperative 800 patients with gynecologic malignancies and indwelling urinary catheters in Zhejiang Cancer Hospital as the research object. The patients were divided into derivation samples (derivation group) and validation samples (validation group) according to the ratio of 3∶1 by random number table method. In the derivation group of 600 cases, the predictive factors were analyzed by binomial Logistic regression analysis and the risk prediction model was developed. In the validation group of 200 cases, the receiver operating characteristic curve was used to evaluate the prediction performance of the model.Results:Binomial Logistic regression analysis showed that age ≥ 60 years old, intraoperative urinary tract injury, postoperative bladder irrigation, and indwelling catheter for more than 7 days were risk factors for CAUTI, and the difference was statistical ( P<0.05) . Model evaluation showed that the area under the receiver operating characteristic curve of the validation group was 0.79 with a statistical difference [95% CI (0.72, 0.87) , P<0.01], and the sensitivity, specificity and Youden index were 71.8%, 78.3% and 50.1% respectively. Conclusions:The prediction performance of the model is good, which helps medical and nursing staff to monitor high-risk patients and reduce the occurrence of CAUTI.
