Department of Orthopedics (Spine Surgery), The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
J Orthop Surg Res. 2021 Apr 21;16(1):274. doi: 10.1186/s13018-021-02425-2.
To develop and validate a nomogram useful in predicting recurrent lumbar disk herniation (rLDH) within 6 months after percutaneous endoscopic lumbar discectomy (PELD).
Information on patients' lumbar disk herniation (LDH) between January 2018 and May 2019 in addition to 26 other features was collected from the authors' hospital. The least absolute shrinkage and selection operator (LASSO) method was used to select the most important risk factors. Moreover, a nomogram was used to build a prediction model using the risk factors selected from LASSO regression. The concordance index (C-index), the receiver operating characteristic (ROC) curve, and calibration curve were used to assess the performance of the model. Finally, clinical usefulness of the nomogram was analyzed using the decision curve and bootstrapping used for internal validation.
Totally, 352 LDH patients were included into this study. Thirty-two patients had recurrence within 6 months while 320 showed no recurrence. Four potential factors, the course of disease, Pfirrmann grade, Modic change, and migration grade, were selected according to the LASSO regression model. Additionally, the C-index of the prediction nomogram was 0.813 (95% CI, 0.726-0.900) and the area under receiver operating characteristic curve (AUC) value was 0.798 while the interval bootstrapping validation C-index was 0.743. Hence, the nomogram might be a good predictive model.
Each variable, the course of disease, Pfirrmann grade, Modic change, and migration grade in the nomogram had a quantitatively corresponding risk score, which can be used in predicting the overall recurrence rate of rLDH within 6 months.
开发和验证一种列线图,用于预测经皮内镜腰椎间盘切除术(PELD)后 6 个月内复发性腰椎间盘突出症(rLDH)。
从作者医院收集了 2018 年 1 月至 2019 年 5 月期间患者腰椎间盘突出症(LDH)的信息,外加 26 个其他特征。最小绝对收缩和选择算子(LASSO)方法用于选择最重要的危险因素。此外,使用 LASSO 回归选择的危险因素,通过列线图构建预测模型。一致性指数(C-index)、接收者操作特征(ROC)曲线和校准曲线用于评估模型的性能。最后,使用决策曲线和内部验证的 bootstrap 分析列线图的临床实用性。
总共纳入了 352 名 LDH 患者。32 名患者在 6 个月内复发,320 名患者无复发。根据 LASSO 回归模型,选择了四个潜在的因素,即病程、Pfirrmann 分级、Modic 改变和迁移分级。此外,预测列线图的 C-index 为 0.813(95%CI,0.726-0.900),ROC 曲线下面积(AUC)值为 0.798,间隔自举验证的 C-index 为 0.743。因此,该列线图可能是一个很好的预测模型。
列线图中的每个变量,即病程、Pfirrmann 分级、Modic 改变和迁移分级,都有一个定量对应的风险评分,可以用于预测 rLDH 在 6 个月内的总体复发率。