Department of Urology, Affiliated Hospital 2 of Nantong University, Nantong, China.
Jiangsu Nantong Urological Clinical Medical Center, Nantong, China.
BMC Cancer. 2024 Sep 3;24(1):1095. doi: 10.1186/s12885-024-12850-1.
One of the most frequent side effects of radical prostatectomy (RP) is urinary incontinence. The primary cause of urine incontinence is usually thought to be impaired urethral sphincter function; nevertheless, the pathophysiology and recovery process of urine incontinence remains unclear. This study aimed to identify potential risk variables, build a risk prediction tool that considers preoperative urodynamic findings, and direct doctors to take necessary action to reduce the likelihood of developing early urinary incontinence.
We retrospectively screened patients who underwent radical prostatectomy between January 1, 2020 and December 31, 2023 at the First People 's Hospital of Nantong, China. According to nomogram results, patients who developed incontinence within three months were classified as having early incontinence. The training group's general characteristics were first screened using univariate logistic analysis, and the LASSO method was applied for the best prediction. Multivariate logistic regression analysis was carried out to determine independent risk factors for early postoperative urine incontinence in the training group and to create nomograms that predict the likelihood of developing early urinary incontinence. The model was internally validated by computing the performance of the validation cohort. The nomogram discrimination, correction, and clinical usefulness were assessed using the c-index, receiver operating characteristic curve, correction plot, and clinical decision curve.
The study involved 142 patients in all. Multivariate logistic regression analysis following RP found seven independent risk variables for early urinary incontinence. A nomogram was constructed based on these independent risk factors. The training and validation groups' c-indices showed that the model had high accuracy and stability. The calibration curve demonstrates that the corrective effect of the training and verification groups is perfect, and the area under the receiver operating characteristic curve indicates great identification capacity. Using a nomogram, the clinical net benefit was maximised within a probability threshold of 0.01-1, according to decision curve analysis (DCA).
The nomogram model created in this study can offer a clear, personalised analysis of the risk of early urine incontinence following RP. It is highly discriminatory and accurate, and it can help create efficient preventative measures and identify high-risk populations.
根治性前列腺切除术（RP）最常见的副作用之一是尿失禁。尿失禁的主要原因通常被认为是尿道括约肌功能受损；然而，尿失禁的病理生理学和恢复过程仍不清楚。本研究旨在确定潜在的风险变量，建立一个考虑术前尿动力学发现的风险预测工具，并指导医生采取必要措施降低发生早期尿失禁的可能性。
我们回顾性筛选了 2020 年 1 月 1 日至 2023 年 12 月 31 日期间在中国南通市第一人民医院接受根治性前列腺切除术的患者。根据列线图结果，将术后 3 个月内发生失禁的患者归类为早期失禁。首先对训练组的一般特征进行单变量逻辑分析，并应用 LASSO 方法进行最佳预测。对训练组进行多变量逻辑回归分析，确定术后早期尿失禁的独立危险因素，并创建预测早期尿失禁可能性的列线图。通过计算验证队列的性能，对模型进行内部验证。通过 C 指数、接收者操作特征曲线、校正图和临床决策曲线评估列线图的区分度、校正和临床实用性。
本研究共涉及 142 例患者。RP 后多变量逻辑回归分析发现，早期尿失禁有七个独立的危险因素。根据这些独立危险因素构建了一个列线图。训练组和验证组的 C 指数表明该模型具有较高的准确性和稳定性。校准曲线表明，训练组和验证组的校正效果非常完美，接收者操作特征曲线下的面积表明具有很好的识别能力。根据决策曲线分析（DCA），使用列线图，在概率阈值为 0.01-1 时，可实现最大的临床净效益。
本研究建立的列线图模型可以对 RP 后早期尿失禁的风险进行清晰、个性化的分析。它具有高度的判别能力和准确性，可以帮助制定有效的预防措施并识别高危人群。
