Zheng Xiaofeng, Xiao Cong, Xie Zhuocheng, Liu Lijuan, Chen Yinhua
Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People's Republic of China.
Department of Orthopedics, Sichuan Science City Hospital, Mianyang, Sichuan, 621000, People's Republic of China.
Int J Gen Med. 2022 Apr 21;15:4339-4356. doi: 10.2147/IJGM.S347425. eCollection 2022.
To establish prediction models for 6-month prognosis in femoral neck-fracture patients receiving total hip arthroplasty (THA).
In total, 182 computed tomography image pairs from 85 patients were collected and divided into a training set (n=127) and testing set (n=55). Least absolute shrinkage-selection operator regression was used for selecting optimal predictors. A random-forest algorithm was used to establish the prediction models, which were evaluated for accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC).
The best model in this study was constructed based on demographic data, preoperative laboratory indicators, and three preoperative radiomic features. In the random-forest model, activated partial thromboplastin time, a preoperative radiomic feature (maximum diameter), and fibrinogen were important variables correlating with patient outcomes. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the training set were 0.986 (95% CI 0.971-1), 0.925 (95% CI 0.862-0.988), 0.983 (95% CI 0.951-1.016), 0.984 (95% CI 0.953-1.014), 0.922 (95% CI 0.856-0.988), and 0.953 (95% CI 0.916-0.990), respectively. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the testing set were 0.949 (95% CI 0.885-1), 0.767 (95% CI 0.615-0.918), 1 (95% CI 1-1), 1 (95% CI 1-1), 0.781 (95% CI 0.638-0.924), and 0.873 (95% CI 0.785-0.961), respectively.
The model based on demographic, preoperative clinical, and preoperative radiomic data showed the best predictive ability for 6-month prognosis in the femoral neck-fracture patients receiving THA.
建立接受全髋关节置换术（THA）的股骨颈骨折患者6个月预后的预测模型。
共收集了85例患者的182对计算机断层扫描图像，并分为训练集（n = 127）和测试集（n = 55）。采用最小绝对收缩选择算子回归来选择最佳预测指标。使用随机森林算法建立预测模型，并对其准确性、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和曲线下面积（AUC）进行评估。
本研究中的最佳模型是基于人口统计学数据、术前实验室指标和三个术前影像组学特征构建的。在随机森林模型中，活化部分凝血活酶时间、一个术前影像组学特征（最大直径）和纤维蛋白原是与患者预后相关的重要变量。训练集的AUC、敏感性、特异性、PPV、NPV和准确性分别为0.986（95%CI 0.971 - 1）、0.925（95%CI 0.862 - 0.988）、0.983（95%CI 0.951 - 1.016）、0.984（95%CI 0.953 - 1.014）、0.922（95%CI 0.856 - 0.988）和0.953（95%CI 0.916 - 0.990）。测试集的AUC、敏感性、特异性、PPV、NPV和准确性分别为0.949（95%CI 0.885 - 1）、0.767（95%CI 0.615 - 0.918）、1（95%CI 1 - 1）、1（95%CI 1 - 1）、0.781（95%CI 0.638 - 0.924）和0.873（95%CI 0.785 - 0.961）。
基于人口统计学、术前临床和术前影像组学数据的模型对接受THA的股骨颈骨折患者6个月预后显示出最佳预测能力。
