PDF(Pubmed)
Abstract：
BACKGROUND: Postoperative urinary retention (POUR), a common condition after prolapse surgery with potential serious sequelae if left untreated, lacks a clearly established optimal timing for catheter removal. This study aimed to develop and validate a predictive model for postoperative urinary retention lasting > 2 and > 4 days after prolapse surgery.
METHODS: We conducted a retrospective review of 1,122 patients undergoing prolapse surgery. The dataset was divided into training and testing cohorts. POUR was defined as the need for continuous intermittent catheterization resulting from a failed spontaneous voiding trial, with passing defined as two consecutive voids ≥ 150 mL and a postvoid residual urine volume ≤ 150 mL. We performed logistic regression and the predicted model was validated using both training and testing cohorts.
RESULTS: Among patients, 31% and 12% experienced POUR lasting > 2 and > 4 days, respectively. Multivariable logistic model identified 6 predictors. For predicting POUR, internal validation using cross-validation approach showed good performance, with accuracy lasting > 2 (area under the curve [AUC] 0.73) and > 4 days (AUC 0.75). Split validation using pre-separated dataset also showed good performance, with accuracy lasting > 2 (AUC 0.73) and > 4 days (AUC 0.74). Calibration curves demonstrated that the model accurately predicted POUR lasting > 2 and > 4 days (from 0 to 80%).
CONCLUSIONS: The proposed prediction model can assist clinicians in personalizing postoperative bladder care for patients undergoing prolapse surgery by providing accurate individual risk estimates.
METHODS: We conducted a retrospective review of 1,122 patients undergoing prolapse surgery. The dataset was divided into training and testing cohorts. POUR was defined as the need for continuous intermittent catheterization resulting from a failed spontaneous voiding trial, with passing defined as two consecutive voids ≥ 150 mL and a postvoid residual urine volume ≤ 150 mL. We performed logistic regression and the predicted model was validated using both training and testing cohorts.
RESULTS: Among patients, 31% and 12% experienced POUR lasting > 2 and > 4 days, respectively. Multivariable logistic model identified 6 predictors. For predicting POUR, internal validation using cross-validation approach showed good performance, with accuracy lasting > 2 (area under the curve [AUC] 0.73) and > 4 days (AUC 0.75). Split validation using pre-separated dataset also showed good performance, with accuracy lasting > 2 (AUC 0.73) and > 4 days (AUC 0.74). Calibration curves demonstrated that the model accurately predicted POUR lasting > 2 and > 4 days (from 0 to 80%).
CONCLUSIONS: The proposed prediction model can assist clinicians in personalizing postoperative bladder care for patients undergoing prolapse surgery by providing accurate individual risk estimates.
摘要：
背景：术后尿潴留（POUR），脱垂手术后的常见病，如果不及时治疗，可能会有严重的后遗症，缺乏明确的导管移除的最佳时机。本研究旨在开发和验证在脱垂手术后持续>2天和>4天的术后尿潴留的预测模型。
方法：我们对1,122例脱垂手术患者进行了回顾性分析。数据集分为训练和测试队列。POUR被定义为由于自发排尿试验失败而需要连续间歇性导管插入。通过定义为两个连续的空隙≥150mL，空隙后残余尿量≤150mL。我们进行了逻辑回归，并使用训练和测试队列验证了预测模型。
结果：在患者中，31%和12%的人经历了持续>2天和>4天的POUR，分别。多变量逻辑模型确定了6个预测因子。为了预测POUR，使用交叉验证方法的内部验证显示出良好的性能，精度持续>2（曲线下面积[AUC]0.73）和>4天（AUC0.75）。使用预分离数据集的拆分验证也显示出良好的性能，准确度持续>2（AUC0.73）和>4天（AUC0.74）。校准曲线表明，该模型准确预测POUR持续>2和>4天（从0到80％）。
结论：所提出的预测模型可以通过提供准确的个体风险估计来帮助临床医生对脱垂手术患者进行个性化的术后膀胱护理。
方法：我们对1,122例脱垂手术患者进行了回顾性分析。数据集分为训练和测试队列。POUR被定义为由于自发排尿试验失败而需要连续间歇性导管插入。通过定义为两个连续的空隙≥150mL，空隙后残余尿量≤150mL。我们进行了逻辑回归，并使用训练和测试队列验证了预测模型。
结果：在患者中，31%和12%的人经历了持续>2天和>4天的POUR，分别。多变量逻辑模型确定了6个预测因子。为了预测POUR，使用交叉验证方法的内部验证显示出良好的性能，精度持续>2（曲线下面积[AUC]0.73）和>4天（AUC0.75）。使用预分离数据集的拆分验证也显示出良好的性能，准确度持续>2（AUC0.73）和>4天（AUC0.74）。校准曲线表明，该模型准确预测POUR持续>2和>4天（从0到80％）。
结论：所提出的预测模型可以通过提供准确的个体风险估计来帮助临床医生对脱垂手术患者进行个性化的术后膀胱护理。
