Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (27): 7135-7141.doi: 10.12307/2026.445
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Dong Ruibo, Tang Dongming, Fei Fenglong, Chen Fengming, Tang Yongnan
Received:2025-10-29
Accepted:2026-01-22
Online:2026-09-28
Published:2026-05-22
Contact:
Dong Ruibo, MS, Attending physician, Department of Lower Limb Orthopedics, Guangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou 510800, Guangdong Province, China
About author:Dong Ruibo, MS, Attending physician, Department of Lower Limb Orthopedics, Guangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou 510800, Guangdong Province, China
CLC Number:
Dong Ruibo, Tang Dongming, Fei Fenglong, Chen Fengming, Tang Yongnan. Relationship between range of motion during distal femoral fracture release surgery and postoperative knee joint function: validation of a multivariate prediction model[J]. Chinese Journal of Tissue Engineering Research, 2026, 30(27): 7135-7141.
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2.4 单因素Logistic回归分析危险因素 将年龄、复位质量、连续被动运动功能锻炼、术后并发症、术中膝关节屈伸活动度、术前胫骨内侧关节线改变程度、术前美国特种外科医院膝关节评分等7个可能与膝关节屈伸功能恢复相关的因素作为自变量纳入单因素Logistic回归分析是,上述7个因素的单因素分析结果显示均有统计学意义(P < 0.05),见图2。 2.5 相关因素的多重共线性检验 多重共线性检验结果显示,纳入的年龄、复位质量、连续被动运动功能锻炼、术后并发症、术中膝关节屈伸活动度、术前胫骨内侧关节线改变程度、术前美国特种外科医院膝关节评分等7个因素容差为0.445-0.757,方差膨胀因子为1.328-2.282,认为多重共线性可能低,见表3。"
2.6 多因素Logistic回归分析危险因素 通过单因素分析及多重共线性检验,可纳入多因素Logistic回归分析的因素包括年龄、复位质量、连续被动运动功能锻炼、术后并发症、术中膝关节屈伸活动度、术前胫骨内侧关节线改变程度、术前美国特种外科医院膝关节评分,其中复位质量为混杂因素(P > 0.05),年龄、连续被动运动功能锻炼、术后并发症、术中膝关节屈伸活动度、术前胫骨内侧关节线改变程度、术前美国特种外科医院膝关节评分均为恢复较差的独立影响因素(P < 0.05),见图3。上述独立影响因素通过Logistic回归进行模型拟合,得到二元回归方程:Logit(p)=1.657+1.861×年龄+1.418×连续被动运动功能锻炼+1.285×术后并发症+1.547×术中膝关节屈伸活动度+1.613×术前美国特种外科医院膝关节评分+2.220×术前胫骨内侧关节线改变程度。 2.7 术中膝关节屈伸活动度与膝关节屈伸功能恢复较差风险的限制性立方样条分析 限制性立方样条模型横坐标为术中膝关节屈伸活动度的连续变化,纵坐标为预测值(OR),阴影为95%CI,限制性立方样条分析显示,术中膝关节屈伸活动度连续变化与恢复较差风险存在非线性关系(非线性检验P < 0.001),见图4。进一步采用阈值效应分析显示,术中膝关节屈伸活动度影响膝关节屈伸功能恢复较差的折点为120°,即术中膝关节屈伸活动度< 120°时,随着其增大,恢复较差风险显著降低;但当术中膝关节屈伸活动度≥120°时,恢复较差风险几乎不再随其增大而降低,见表4。"
2.8 Logistic回归预测模型的诊断与评价 2.8.1 强影响点分析 对回归模型行强影响点分析,结果显示,Cook距离均< 1,表明建模所使用的数据中不存在对模型参数估计产生强烈影响的数据点,见图5。 2.8.2 评价模型区分度 受试者工作特征曲线分析显示,预测模型的曲线下面积为0.821(95%CI:0.760-0.953),表明其区分能力良好,灵敏度为0.875,特异度为0.764,见图6。 2.8.3 评价模型的预测精度 校准曲线分析显示,预测模型的预测概率与实际概率近似,Brier值=0.097,表明预测能力较好,见图7。 2.8.4 模型的临床可用性评价 决策曲线分析显示,阈概率在0.1-0.9范围内,预测模型的净收益较高。结果表明应用该模型筛选高风险人群并实施临床干预措施,可获得较高的临床收益,见图8。"
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