Huang Yixin, Lin Dongze, Chen Bin, Jiang Xiaole, Shangguan Shanglin, Lin Fengfei
Department of Orthopedics, Fuzhou Second General Hospital, Fujian Provincial Clinical Medical Research Center for Trauma Orthopedics Emergency and Rehabilitation, Fuzhou, China.
Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Front Surg. 2025 Aug 26;12:1591671. doi: 10.3389/fsurg.2025.1591671. eCollection 2025.
Femoral neck fractures are the most common type of hip fracture, and the postoperative complications associated with these fractures significantly affect patients' quality of life and healthcare costs. The objective of this study was to develop a predictive model using machine learning (ML) techniques to assess the risk of postoperative complications in young and middle-aged patients with femoral neck fractures.
We retrospectively analyzed data from 899 young and middle-aged patients with femoral neck fractures who underwent surgical treatment between September 2019 and June 2024. Key predictors affecting postoperative complications were identified through LASSO regression and multifactorial logistic regression analyses. Several machine learning (ML) models were then integrated for comparative analysis. Ultimately, the best-performing model was selected, and its interpretation was provided using SHAP values to offer a personalized risk assessment.
The study results indicate that intraoperative reduction quality, medial cortex comminution, fracture types, posterior tilt angle, early postoperative weight-bearing, and removal of internal fixation devices are significant predictors of postoperative complications. The logistic regression model demonstrated the best performance on the test set, with an area under the curve (AUC) of 0.906, accuracy of 0.877, sensitivity of 0.748, and specificity of 0.903. Additionally, SHAP analysis identified the seven most important features in the model, providing clinicians with an intuitive tool for risk assessment.
This study successfully developed and validated a logistic regression-based predictive model, augmented with SHAP explanations, providing an effective tool for assessing the risk of postoperative complications in young and middle-aged patients with femoral neck fractures.
股骨颈骨折是最常见的髋部骨折类型，与这些骨折相关的术后并发症会显著影响患者的生活质量和医疗费用。本研究的目的是使用机器学习（ML）技术开发一种预测模型，以评估中青年股骨颈骨折患者术后并发症的风险。
我们回顾性分析了2019年9月至2024年6月期间接受手术治疗的899例中青年股骨颈骨折患者的数据。通过LASSO回归和多因素逻辑回归分析确定影响术后并发症的关键预测因素。然后整合多个机器学习（ML）模型进行比较分析。最终，选择性能最佳的模型，并使用SHAP值进行解释，以提供个性化的风险评估。
研究结果表明，术中复位质量、内侧皮质粉碎、骨折类型、后倾角、术后早期负重以及内固定装置取出是术后并发症的重要预测因素。逻辑回归模型在测试集上表现最佳，曲线下面积（AUC）为0.906，准确率为0.877，灵敏度为0.748，特异性为0.903。此外，SHAP分析确定了模型中七个最重要的特征，为临床医生提供了一个直观的风险评估工具。
本研究成功开发并验证了一种基于逻辑回归的预测模型，并辅以SHAP解释，为评估中青年股骨颈骨折患者术后并发症风险提供了一种有效的工具。
