混合痔外剥内扎切除术后发生尿潴留的危险因素分析及预测模型的构建OA

Objective:To investigate the influencing factors of postoperative urinary retention in patients undergoing Milligan-Morgan hemorrhoidectomy for mixed hemorrhoids,and to establish a nomogram risk warning model.Method:A total of 140 patients who underwent Milligan-Morgan hemorrhoidectomy for mixed hemorrhoids at the Affiliated Hospital of Jiujiang University from Apr 2022 to Apr 2024 were selected as the training set.According to whether postoperative urinary retention occurred,the patients were divided into the urinary retention group(n=70)and the non-urinary retention group(n=70).Another 60 patients during the same period were selected as the validation set according to the ratio of 7∶3 between the training set and the validation set.Clinical data of the patients were collected.Univariable analysis was performed first,followed by binary logistic regression to screen independent risk factors.A nomogram risk warning model was constructed,and the validation set data was used for model validation and efficacy evaluation.Result:Binary logistic regression analysis showed that seven factors,including patient age≥60 years,male gender,longer time for anesthesia recovery,infusion volume≥1 000 ml,indwelling catheter time≥72 h,longer time for first postoperative urination,and postoperative urinary tract infection,were identified as risk factors for postoperative urinary retention(P＜0.05).The established nomogram risk prediction model was verified and demonstrated good performance.The area under the receiver operating characteristic(ROC)curve(AUC)of the training set was 0.912(95%CI:0.868-0.957),with a sensitivity of 91.4%and a specificity of 77.1%.The calibration curve showed good fit(mean absolute error=0.034).The AUC of the validation set was 0.913(95%CI:0.843-0.983),with a sensitivity of 93.3%and a specificity of 76.7%.The calibration curve also showed good fit(mean absolute error=0.049).Conclusions:The above seven risk factors have high predictive value for postoperative urinary retention.The warning model can provide data support for the screening of high-risk patients with postoperative urinary retention.