Department of Orthopaedic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China.
Third Hospital of Shanxi Medical University, No. 99, Longcheng street, Taiyuan city, 030032, Shanxi Province, China.
BMC Musculoskelet Disord. 2023 Oct 13;24(1):813. doi: 10.1186/s12891-023-06816-w.
Postoperative urine retention (POUR) after lumbar interbody fusion surgery may lead to recatheterization and prolonged hospitalization. In this study, a predictive model was constructed and validated. The objective was to provide a nomogram for estimating the risk of POUR and then reducing the incidence.
A total of 423 cases of lumbar fusion surgery were included; 65 of these cases developed POUR, an incidence of 15.4%. The dataset is divided into a training set and a validation set according to time. 18 candidate variables were selected. The candidate variables were screened through LASSO regression. The stepwise regression and random forest analysis were then conducted to construct the predictive model and draw a nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve were used to evaluate the predictive effect of the model.
The best lambda value in LASSO was 0.025082; according to this, five significant variables were screened, including age, smoking history, surgical method, operative time, and visual analog scale (VAS) score of postoperative low back pain. A predictive model containing four variables was constructed by stepwise regression. The variables included age (β = 0.047, OR = 1.048), smoking history (β = 1.950, OR = 7.031), operative time (β = 0.022, OR = 1.022), and postoperative VAS score of low back pain (β = 2.554, OR = 12.858). A nomogram was drawn based on the results. The AUC of the ROC curve of the training set was 0.891, the validation set was 0.854 in the stepwise regression model. The calibration curves of the training set and validation set are in good agreement with the actual curves, showing that the stepwise regression model has good prediction ability. The AUC of the training set was 0.996, and that of the verification set was 0.856 in the random forest model.
This study developed and internally validated a new nomogram and a random forest model for predicting the risk of POUR after lumbar interbody fusion surgery. Both of the nomogram and the random forest model have high accuracy in this study.
腰椎体间融合术后尿潴留（POUR）可能导致再次导尿和住院时间延长。本研究构建并验证了一个预测模型，旨在提供一种用于估计 POUR 风险的列线图，从而降低其发生率。
共纳入 423 例腰椎融合手术患者，其中 65 例发生 POUR，发生率为 15.4%。数据集根据时间分为训练集和验证集。选择 18 个候选变量，通过 LASSO 回归筛选候选变量，然后进行逐步回归和随机森林分析，构建预测模型并绘制列线图。采用受试者工作特征（ROC）曲线下面积（AUC）和校准曲线评估模型的预测效果。
LASSO 中的最佳 lambda 值为 0.025082，根据该值筛选出 5 个有意义的变量，包括年龄、吸烟史、手术方式、手术时间和术后腰痛视觉模拟评分（VAS）。通过逐步回归构建了包含 4 个变量的预测模型，包括年龄（β=0.047，OR=1.048）、吸烟史（β=1.950，OR=7.031）、手术时间（β=0.022，OR=1.022）和术后腰痛 VAS 评分（β=2.554，OR=12.858）。根据结果绘制了列线图。训练集的 ROC 曲线 AUC 为 0.891，验证集为 0.854。训练集和验证集的校准曲线与实际曲线吻合良好，表明逐步回归模型具有良好的预测能力。训练集的 AUC 为 0.996，验证集的 AUC 为 0.856。
本研究建立并内部验证了一种新的列线图和随机森林模型，用于预测腰椎体间融合术后 POUR 的风险。在本研究中，列线图和随机森林模型都具有较高的准确性。
