Factors influencing postoperative urinary retention after radical hysterectomy for cervical cancer: development and validation of a predictive model in a prospective cohort study in Southwest China - Kaixuan Yang; Jianjun Zhang; Yan Zuo; Xiaolin Hu; Xinru Liu; Zhilan Bai; Jingwen He (BMJ Open)
Objectives To explore influencing factors for postoperative urinary retention (POUR) in cervical cancer patients and construct and validate a POUR prediction model.Design A prospective cohort study.Setting A large tertiary hospital specialised in child and maternal healthcare in Southwest China.Participants 1101 patients undergoing cervical cancer surgery at our hospital were enrolled in the analytic cohort between 1 July 2022 and 31 July 2023. Another 205 patients were enrolled in the external validation cohort between 1 August 2023 and 31 October 2023. Demographics and disease-related information were collected to construct a risk prediction model by logistic regression. Univariate analyses and a multivariate logistic regression analysis were conducted to determine possible influencing factors. The discrimination and accuracy of the model were assessed by the area under the curve (AUC) and the concordance index, respectively.Results Univariate analysis identified duration of surgery, intraoperative bleeding, presence of diabetes, hypertension, ureteral adhesion, wound healing classification, preoperative radio/chemotherapy, category of Body Mass Index, history of urinary diseases, history of caesarean section, postoperative urinary infection and use of analgesia pumps as potential influencing factors (p<0.05). Diabetes, wound healing classification, presurgery radio/chemotherapy, postoperative urinary infection, use of analgesia pumps and pain numerical rating score were founded to be significant factors influencing the occurrence of POUR in cervical cancer patients (p<0.05). A POUR prediction model constructed using the factors demonstrated excellent prediction power, with an AUC of 0.897 (95% CI, 0.877 to 0.916, p<0.001). The sensitivity of the model at the optimal threshold was 0.591, with specificity being 0.747. The receiver operating characteristic curve indicated a good performance of the model.Conclusions Presence of diabetes mellitus, wound healing classification, presurgery radio/chemotherapy, postoperative urinary infection, use of analgesia pumps and pain numerical rating score are factors influencing occurrence of POUR in cervical cancer patients. The POUR prediction model developed demonstrates good predictive power and is promising for clinical utility.