Ying Yao,1,* Lijun Tao,1,* Li Ma,2 Yuhan Fan,3 Dongju Zheng,2 Jian Wang,1 Wen Chen1 

1Department of Pharmacy, People’s Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 2Department of Radiotherapy, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, People’s Republic of China; 3Department of Preparation Center, Ningxia Medical University General Hospital, Yinchuan, Ningxia, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Wen Chen, Department of Pharmacy, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, People’s Republic of China, Tel +86 19995350858, Email chenwen1986221@163.com

Objective: To investigate the distribution characteristics of pathogenic bacteria causing urinary tract infections in cervical cancer patients undergoing intensity-modulated radiotherapy (IMRT). Furthermore, to explore the risk factors and predictive factors associated with urinary tract infections, and to establish a personalized risk prediction model.
Methods: A retrospective study analyzed 160 cervical cancer patients undergoing intensity-modulated radiotherapy at the People’s Hospital of Ningxia Hui Autonomous Region from 2020 to 2023. The clinical characteristics of the participants were collected, and in combination with microbiological culture results, the distribution and drug resistance of pathogens causing urinary tract infections were analyzed. Using logistic regression and multivariable logistic analysis, we established a predictive model that includes clinical variables.
Results: Urinary specimens were collected and analyzed from 52 patients with urinary tract infections. The incidence of urinary tract infections in cervical cancer patients after radiotherapy in this study was approximately 32.5%, with the predominant pathogens identified as E. coli, E. faecalis, E. faecium, and P. mirabilis. Invasive procedures (OR 4.202, 95% CI:1.003– 17.608; P=0.050), history of ureteral stent insertion (OR 7.260, 95% CI:2.026– 26.016; P=0.002), Concurrent chemotherapy (OR 2.587, 95% CI:1.010– 6.623; P=0.048), and low serum albumin levels (OR 0.842, 95% CI:0.745– 0.951; P=0.006) were identified as four key factors in the final predictive model. The calibration curve indicated a consistent alignment between the predicted probabilities from the nomogram model and the actual observed outcomes. With an AUC of 0.804 (95% CI: 0.727– 0.881) for the ROC curve, the nomogram prediction model demonstrated strong predictive performance.
Conclusion: E. coli remains the most common pathogen causing urinary tract infections in cervical cancer patients with IMRT. The history of ureteral stent insertion, invasive procedures, concurrent chemotherapy, and serum albumin levels have been identified as independent risk factors of urinary tract infections in cervical cancer IMRT patients. The nomogram prediction model based on these factors can serve as a reference for clinicians to help prevent urinary tract infections.

Keywords: cervical cancer, urinary tract infections, IMRT, nomogram, model