吴悦棋, 丁雪飞, 栾阳, 朱良勇, 谈啸, 吴振豪. 机器人辅助前列腺癌根治术后即刻社会性尿控恢复预测模型的构建和验证[J]. 实用临床医药杂志, 2024, 28(10): 1-4,12. DOI: 10.7619/jcmp.20232820
引用本文: 吴悦棋, 丁雪飞, 栾阳, 朱良勇, 谈啸, 吴振豪. 机器人辅助前列腺癌根治术后即刻社会性尿控恢复预测模型的构建和验证[J]. 实用临床医药杂志, 2024, 28(10): 1-4,12. DOI: 10.7619/jcmp.20232820
WU Yueqi, DING Xuefei, LUAN Yang, ZHU Liangyong, TAN Xiao, WU Zhenhao. Construction and verification of a predictive model for immediate social urinary control recovery after robot assisted radical prostatectomy for prostate cancer[J]. Journal of Clinical Medicine in Practice, 2024, 28(10): 1-4,12. DOI: 10.7619/jcmp.20232820
Citation: WU Yueqi, DING Xuefei, LUAN Yang, ZHU Liangyong, TAN Xiao, WU Zhenhao. Construction and verification of a predictive model for immediate social urinary control recovery after robot assisted radical prostatectomy for prostate cancer[J]. Journal of Clinical Medicine in Practice, 2024, 28(10): 1-4,12. DOI: 10.7619/jcmp.20232820

机器人辅助前列腺癌根治术后即刻社会性尿控恢复预测模型的构建和验证

Construction and verification of a predictive model for immediate social urinary control recovery after robot assisted radical prostatectomy for prostate cancer

  • 摘要: 目的 构建并验证预测机器人辅助前列腺癌根治术(RARP)患者拔管后即刻社会性尿控恢复情况的列线图模型。方法 回顾性分析确诊前列腺癌并由单一术者行手术治疗的64例患者的临床资料,评估患者拔除尿管后的即刻社会性尿控恢复情况。采用LASSO回归进行特征筛选,将选取的特征进行多元Logistic回归分析,确定独立危险因素,并构建列线图模型。采用受试者工作特征(ROC)曲线、Hosmer-Lemeshow检验和校准曲线、临床决策曲线(DCA)分析模型的鉴别性、校准性和临床实用性。结果 构建列线图模型的变量包括D'Amico分级、外提肌距离。ROC曲线的曲线下面积(AUC)为0.742(95%CI: 0.500~0.913, P<0.001),表明该模型具有较好的鉴别性; 校准曲线表明该模型具有较好的校准能力; DCA显示该模型具有较好的临床实用性。结论 本研究构建的列线图模型可以预测RARP患者术后即刻社会性尿控恢复情况,能够进一步量化即刻达到社会性尿控的概率。

     

    Abstract: Objective To construct and verify a nomogram model for predicting social urinary control recovery in patients undergoing robot-assisted radical prostatectomy (RARP) immediately after extubation. Methods A retrospective analysis was conducted on the clinical data of 64 patients diagnosed with prostate cancer and treated by a single surgeon. The immediate urinary control status of the patients after removal of the catheter was evaluated, and LASSO regression was used for feature screening. Multiple Logistic regression was performed on the selected features to determine independent risk factors and establish a predictive model. And the discriminability, calibration, and clinical practicality of the model were evaluated using receiver operating curve (ROC), Hosmer Lemeshow test and calibration curve, and clinical decision curve (DCA) analysis. Results The variables in the outcome prediction model include D'Amico grading and distance of the levator muscle. The area under the ROC curve (AUC) was 0.742 (95%CI, 0.500 to 0.913, P<0.001), indicating that the model had good discriminability. The calibration curve indicated that the model had good calibration ability. The DCA curve showed good clinical practicality. Conclusion The clinical predictive model developed inthis study can predict the recovery of immediate social urinary control in patients with RARP after surgery, which can further quantify the probability of achieving immediate social continence.

     

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