Xu Zhe, Chen Qiuhan, Zhou Zhi, Sun Jianbo, Tian Guang, Liu Chen, Hou Guangzhi, Zhang Ruguo
Department of Orthopedics, Guihang Guiyang 300 Hospital, Guiyang, 550004, China.
Guizhou Medical University, Guiyang, 550004, China.
BMC Musculoskelet Disord. 2025 Apr 22;26(1):403. doi: 10.1186/s12891-025-08619-7.
The wedge effect (V-effect) is a common complication in intramedullary nailing surgery for intertrochanteric fractures and can significantly affect postoperative outcomes. The purpose of this study was to screen risk factors for the intraoperative V-effect in intertrochanteric fractures and to develop a clinical prediction model.
A total of 319 patients (77 patients who developed V-effects) from China were randomly divided into a training set (n = 223) and a validation set (n = 96) at a ratio of 7:3. The variables were screened via 3 machine learning methods, including least absolute shrinkage and selection operator (LASSO) regression, the Boruta algorithm, and recursive feature elimination (RFE). Variables that appeared in the three machine learning methods were included in multivariate logistic regression to construct predictive models. Spearman correlation analysis was used to exclude covariance between variables. Restricted cubic splines (RCSs) were used to analyze the relationships among femoral lateral wall thickness, BMI, and the V effect. The differentiation, calibration and clinical applicability of the model were assessed, and the reasonability of the model was analyzed.
Machine learning identified 8 variables that appeared in these 3 machine learning methods, and the covariance between these 8 variables was excluded (r < 0.6). BMI, surgical experience, a lesser trochanteric fracture, the thickness of the lateral wall, the insertion point, bone density, fracture classification, and holiday surgery were found to be risk factors for the occurrence of the V-effect via multivariate logistic regression. The RCS analysis revealed that the lateral wall thickness, BMI, and occurrence of the V effect were linearly related. The final predictive model had good differentiation, calibration and clinical applicability, and it had better predictive efficacy than the other models did.
This study employed three machine learning variable selection methods-the LASSO, RFE, and Boruta algorithms-to construct a V-effect predictive model. The model enables orthopedic surgeons to better understand the risk factors associated with the V-effect and provides a reference for surgeons to implement appropriate measures to reduce the incidence of the V-effect.
楔形效应（V效应）是股骨转子间骨折髓内钉手术中常见的并发症，可显著影响术后结果。本研究旨在筛选股骨转子间骨折术中V效应的危险因素，并建立临床预测模型。
来自中国的319例患者（77例发生V效应）按7:3的比例随机分为训练集（n = 223）和验证集（n = 96）。通过3种机器学习方法筛选变量，包括最小绝对收缩和选择算子（LASSO）回归、Boruta算法和递归特征消除（RFE）。将在3种机器学习方法中出现的变量纳入多因素逻辑回归以构建预测模型。采用Spearman相关性分析排除变量间的共线性。使用限制立方样条（RCS）分析股骨外侧壁厚度、体重指数（BMI）与V效应之间的关系。评估模型的区分度、校准度和临床适用性，并分析模型的合理性。
机器学习确定了在这3种机器学习方法中均出现的8个变量，并排除了这8个变量间的共线性（r < 0.6）。通过多因素逻辑回归发现，BMI、手术经验、小转子骨折、外侧壁厚度、进针点、骨密度、骨折分型和节假日手术是发生V效应的危险因素。RCS分析显示，外侧壁厚度、BMI与V效应的发生呈线性相关。最终的预测模型具有良好的区分度、校准度和临床适用性，且预测效能优于其他模型。
本研究采用3种机器学习变量选择方法——LASSO、RFE和Boruta算法——构建V效应预测模型。该模型使骨科医生能够更好地了解与V效应相关的危险因素，并为外科医生采取适当措施降低V效应的发生率提供参考。
