Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (3): 626-633.doi: 10.12307/2025.866
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Liao Long1, Zhao Zepeng2, Li Zongyuan1, Yu Qinglong2, Zhang Tao2, Tang Jinyuan2, Ye Nan1, Xu Han1, Shi Bo1
Received:
2024-10-09
Accepted:
2024-12-18
Online:
2026-01-28
Published:
2025-07-04
Contact:
Shi Bo, MS, Chief physician, Department of Orthopedics of Mianyang Central Hospital, Mianyang Hospital Affiliated to School of Medicine of University of Electronic Science and Technology of China, Mianyang 621000, Sichuan Province, China
About author:
Liao Long, Master candidate, Physician, Department of Orthopedics of Mianyang Central Hospital, Mianyang Hospital Affiliated to School of Medicine of University of Electronic Science and Technology of China, Mianyang 621000, Sichuan Province, China
CLC Number:
Liao Long, Zhao Zepeng, Li Zongyuan, Yu Qinglong, Zhang Tao, Tang Jinyuan, Ye Nan, Xu Han, Shi Bo. Establishment and validation of a model for femoral head necrosis after internal fixation of femoral neck fracture using logistic regression and SHAP analysis[J]. Chinese Journal of Tissue Engineering Research, 2026, 30(3): 626-633.
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2.6 预测模型的评价及内部验证 使用受试者工作特征曲线分析和Hosmer-Lemeshow测试来评估模型的判别能力和校准能力。预测模型的AUC为0.940(95%CI:0.903-0.977),灵敏度为90.2%,特异度为87.6%,证明模型区分度良好(图4A);H-L检验显示,χ2=6.59,P=0.581 > 0.05,说明模型校准度良好;通过绘制校准曲线来评估列线图的准确性,该模型与理想模型的重合程度良好(图4B)。结果显示,决策曲线分析法通过直观方式证实了风险预测模型在临床实践中的有效性。预测模型的最大净收益阈值概率范围为1%-92%(图4C)。采用Bootstrap法重抽样1 000次,对模型进行内部验证,重抽样后,预测模型的AUC为0.939,模型鉴别效能依然良好。 2.7 SHAP分析 为了进一步研究股骨颈骨折内固定术后股骨头坏死的主要影响因素,此次研究通过SHAP方法对数据集特征进行分析,其成果展示于图5中,详细说明了各特征的重要性。骨折线位置、复位质量、手术时间以及移位情况是影响内固定术后股骨头坏死的重要因素,其中以骨折线位置最为重要。对于划分好的数据,此文借助Python中的SHAP库计算了模型的SHAP值,如图6所示,每个点对应数据集的一个样本。x轴上的位置、即实际的SHAP值,表示该特征对特定样本的模型输出、即对特定样本的相对患病风险的影响。换言之,较高SHAP值的样本相对于较低SHAP值的样本具有较高的股骨头坏死风险。此外,各个特征按其重要性沿y轴排列,其重要性由其绝对SHAP值的平均值给出,特征位置越高,说明重要性越高。"
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