Lin Chun, Liang Zhen, Liu Jianfeng, Sun Wei
Department of Orthopedics, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Department of General Medicine and Geriatrics, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China.
Front Surg. 2023 Jun 7;10:1160085. doi: 10.3389/fsurg.2023.1160085. eCollection 2023.
Machine learning (ML) has been widely utilized for constructing high-performance prediction models. This study aimed to develop a preoperative machine learning-based prediction model to identify functional recovery one year after hip fracture surgery.
We collected data from 176 elderly hip fracture patients admitted to the Department of Orthopaedics and Oncology at Shenzhen Second People's Hospital between May 2019 and December 2019, who met the inclusion criteria. Patient's functional recovery was monitored for one year after surgery. We selected 26 factors, comprising 12 preoperative indicators, 8 surgical indicators, and 6 postoperative indicators. Eventually, 77 patients were included based on the exclusion criteria. Random allocation divided them into the training set (70%) and test set (30%) for internal validation. The Lasso method was employed to screen prognostic variables. We conducted comparisons among various common machine learning classifiers to determine the best prediction model. Prediction performance was evaluated using the area under the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis. To identify the importance of the predictor variables, we performed the recursive feature elimination (RFE) algorithm based on Shapley Additive Explanations (SHAP) values.
The AUCs for the testing dataset were as follows: logistic regression (Logit) model = 0.934, k-nearest neighbors (KNN) model = 0.930, support vector machine (SVM) model = 0.910, Gaussian naive Bayes (GNB) model = 0.926, decision tree (DT) model = 0.730, random forest (RF) model = 0.957, and Extreme Gradient Boosting (XGB) model = 0.902. Among the seven ML-based models tested, the RF model demonstrated the best prediction performance, incorporating four features: postoperative rehabilitation compliance, marital status, age-adjusted Charlson comorbidity score (aCCI), and clinical frailty scale (CFS).
We developed a prediction model for the functional recovery following hip fracture surgery in elderly patients after one year, based on the Random Forest (RF) algorithm. This model exhibited superior prediction performance (ROC) compared to other models. The software application is available for use. External validation in a larger patient cohort or diverse hospital settings is necessary to assess the clinical utility of this tool.
机器学习（ML）已被广泛用于构建高性能预测模型。本研究旨在开发一种基于术前机器学习的预测模型，以识别髋部骨折手术后一年的功能恢复情况。
我们收集了2019年5月至2019年12月期间深圳市第二人民医院骨科和肿瘤科收治的176例符合纳入标准的老年髋部骨折患者的数据。术后对患者的功能恢复情况进行了一年的监测。我们选择了26个因素，包括12个术前指标、8个手术指标和6个术后指标。最终，根据排除标准纳入77例患者。随机分配将他们分为训练集（70%）和测试集（30%）进行内部验证。采用Lasso方法筛选预后变量。我们对各种常见的机器学习分类器进行了比较，以确定最佳预测模型。使用受试者操作特征曲线（ROC）下的面积、校准曲线和决策曲线分析来评估预测性能。为了确定预测变量的重要性，我们基于Shapley加法解释（SHAP）值执行了递归特征消除（RFE）算法。
测试数据集的AUC如下：逻辑回归（Logit）模型=0.934，k近邻（KNN）模型=0.930，支持向量机（SVM）模型=0.910，高斯朴素贝叶斯（GNB）模型=0.926，决策树（DT）模型=0.730随机森林（RF）模型=0.957，极端梯度提升（XGB）模型=0.902。在测试的七个基于ML的模型中，RF模型表现出最佳的预测性能，纳入了四个特征：术后康复依从性、婚姻状况、年龄调整后的Charlson合并症评分（aCCI）和临床衰弱量表（CFS）。
我们基于随机森林（RF）算法开发了一种老年患者髋部骨折手术后一年功能恢复的预测模型。该模型与其他模型相比表现出卓越的预测性能（ROC）。该软件应用可供使用。需要在更大的患者队列或不同的医院环境中进行外部验证，以评估该工具的临床实用性。
