[1]孟杰,郑笑天,于文君,等.股骨颈骨折内固定术后股骨头坏死风险预测列线图模型构建[J].中医正骨,2026,38(03):22-29.
　MENG Jie,ZHENG Xiaotian,YU Wenjun,et al.Construction of a nomogram model for predicting the risk of femoral head necrosis after internal fixation of femoral neck fractures[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2026,38(03):22-29.
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股骨颈骨折内固定术后股骨头坏死风险预测列线图模型构建()

《中医正骨》[ISSN:1001-6015/CN:41-1162/R]

卷:
第38卷
期数:
2026年03期
页码:
22-29
栏目:
临床研究
出版日期:
2026-03-20

文章信息/Info

Title:
Construction of a nomogram model for predicting the risk of femoral head necrosis after internal fixation of femoral neck fractures
作者:
孟杰; 郑笑天; 于文君; 刘冬雪; 詹文超; 姜向南
(吉林大学第一医院,吉林 长春 130031)
Author(s):
MENG Jie; ZHENG Xiaotian; YU Wenjun; LIU Dongxue; ZHAN Wenchao; JIANG Xiangnan
The First Hospital of Jilin University,Changchun 130031,Jilin,China
关键词:
股骨颈骨折;  股骨头坏死;  骨折固定术; 内;  风险;  预测;  列线图表
Keywords:
femoral neck fractures;  femur head necrosis;  fracture fixation; internal;  risk;  forecasting;  nomograms
摘要:
目的:建立股骨颈骨折内固定术后股骨头坏死(osteonecrosis of femoral head,ONFH)风险预测列线图模型。方法:选取2020年1月至2022年6月和2022年7月至2023年5月收治的股骨颈骨折患者分别作为训练集和验证集。所有患者均采用内固定手术治疗,并观察术后12个月内发生ONFH的情况。根据是否发生ONFH,将训练集患者分为ONFH组和无ONFH组,比较2组患者的年龄、性别、体质量指数、吸烟史、合并糖尿病情况、合并高血压病情况、骨折原因、骨折侧别、Garden分型、骨折线位置、受伤至手术时间、复位方式、复位质量、术前牵引情况、手术至下地负重时间及内固定取出情况,筛选股骨颈骨折内固定术后ONFH的影响因素。采用多因素Logistic回归分析进一步筛选股骨颈骨折内固定术后ONFH的独立影响因素。在此基础上构建股骨颈骨折内固定术后ONFH风险预测列线图模型; 基于训练集和验证集数据,以受试者操作特征(receiver operating characteristic,ROC)曲线、校准曲线和决策曲线评估模型的预测性能。结果:①一般情况。共纳入股骨颈骨折患者394例,训练集276例(ONFH组67例、无ONFH组209例)、验证集118例。②股骨颈骨折内固定术后ONFH的影响因素分析结果。ONFH组和无ONFH组合并糖尿病情况、Garden分型、术前牵引情况、复位质量及内固定取出情况的组间差异均有统计学意义(χ2=7.073,P=0.008; χ2=10.313,P=0.001; χ2=9.914,P=0.002; χ2=9.180,P=0.002; χ2=6.684,P=0.010),年龄、性别、体质量指数、吸烟史、合并高血压病情况、骨折原因、骨折侧别、骨折线位置、受伤至手术时间、复位方式及手术至下地负重时间的组间差异均无统计学意义。多因素Logistic回归分析结果显示,合并糖尿病情况、Garden分型、术前牵引情况、复位质量及内固定取出情况均为股骨颈骨折内固定术后ONFH的独立影响因素[B=1.102,P=0.004,OR=3.010(1.410,6.429); B=1.101,P=0.001,OR=3.007(1.534,5.894); B=1.030,P=0.001,OR=2.800(1.495,5.244); B=1.312,P=0.002,OR=3.714(1.592,8.669); B=1.195,P=0.001,OR=3.304(1.627,6.711)]。③股骨颈骨折内固定术后ONFH风险预测列线图模型构建结果。基于合并糖尿病情况、Garden分型、术前牵引情况、复位质量及内固定取出情况5个变量,构建股骨颈骨折内固定术后ONFH风险预测静态列线图模型和动态列线图模型(https://cooldongtai.shinyapps.io/dynnomapp-2)。该模型预测训练集和验证集股骨颈骨折内固定术后ONFH的ROC曲线下面积分别为0.750和0.781; 训练集与验证集的校准曲线均显示模型预测值与实际发生值基本一致; 决策曲线显示,当阈值概率为7%～61%时,该模型对股骨颈骨折内固定术后ONFH的预测具有良好的临床净获益。结论:基于合并糖尿病情况、Garden分型、术前牵引情况、复位质量及内固定取出情况5个变量构建的股骨颈骨折内固定术后ONFH风险预测列线图模型,具有中等预测效能与良好的临床净获益,可为临床个体化评估股骨颈骨折内固定术后ONFH发生风险提供一定参考。
Abstract:
Objective:To establish a risk prediction nomogram model for osteonecrosis of femoral head(ONFH)after internal fixation of femoral neck fractures(FNFs).Methods:The patients with FNFs admitted from January 2020 to June 2022 and from July 2022 to May 2023 were selected as the training set and validation set,respectively.All patients were treated with internal fixation surgery,and the occurrence of ONFH within 12 months after surgery was observed.According to whether ONFH occurred,patients in the training set were further divided into an ONFH group and a non-ONFH group.The age,gender,body mass index(BMI),smoking history,comorbid diabetes mellitus,comorbid hypertension,fracture cause,fracture side,Garden classification,fracture line position,time from injury to surgery,reduction method,reduction quality,preoperative traction,time from surgery to weight-bearing,and internal fixation removal were compared between the two groups to screen the influencing factors of ONFH after internal fixation of FNFs.Multivariate logistic regression analysis was performed to further screen the independent influencing factors of ONFH after internal fixation of FNFs.Based on the screened factors,a risk prediction nomogram model for ONFH after internal fixation of FNFs was constructed.Based on the data of the training set and validation set,the predictive performance of the model was evaluated using the receiver operating characteristic(ROC)curve,calibration curve,and decision curve,respectively.Results:①General situation.A total of 394 FNFs patients were included,with 276 cases in the training set(67 cases in the ONFH group,209 cases in the non-ONFH group)and 118 cases in the validation set.②Results of analysis on influencing factors of ONFH after internal fixation of FNFs.The differences in comorbid diabetes mellitus,Garden classification,preoperative traction,reduction quality,and internal fixation removal between the ONFH group and the non-ONFH group were statistically significant(χ2=7.073,P=0.008; χ2=10.313,P=0.001; χ2=9.914,P=0.002; χ2=9.180,P=0.002; χ2=6.684,P=0.010).The differences in age,gender,BMI,smoking history,comorbid hypertension,fracture cause,fracture side,fracture line position,time from injury to surgery,reduction method,and time from surgery to weight-bearing were not statistically significant.Multivariate Logistic regression analysis showed that comorbid diabetes mellitus,Garden classification,preoperative traction,reduction quality,and internal fixation removal were the independent influencing factors for ONFH after internal fixation of FNFs(B=1.102,P=0.004,OR=3.010(1.410,6.429); B=1.101,P=0.001,OR=3.007(1.534,5.894); B=1.030,P=0.001,OR=2.800(1.495,5.244); B=1.312,P=0.002,OR=3.714(1.592,8.669); B=1.195,P=0.001,OR=3.304(1.627,6.711)).③Construction results of the ONFH risk prediction nomogram model after internal fixation of FNFs.Based on the five variables of comorbid diabetes mellitus,Garden classification,preoperative traction,reduction quality,and internal fixation removal,a static nomogram model and a dynamic nomogram model for predicting ONFH risk after internal fixation of FNFs were constructed,respectively(https://cooldongtai.shinyapps.io/dynnomapp-2).The area under the ROC curve of this model for predicting ONFH after internal fixation of FNFs in the training set and validation set was 0.750 and 0.781,respectively; the calibration curves of both the training set and validation set showed that the predicted values of the model were basically consistent with the actual occurrence values; the decision curve showed that when the threshold probability was 7%-61%,the model had good clinical net benefit in predicting ONFH after internal fixation of FNFs.Conclusion:The ONFH risk prediction nomogram model after internal fixation of FNFs constructed based on the five variables of comorbid diabetes mellitus,Garden classification,preoperative traction,reduction quality,and internal fixation removal has moderate predictive performance and good clinical net benefit,which can provide a certain reference for clinical individualized assessment of the risk of ONFH after internal fixation of FNFs.

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备注/Memo

备注/Memo:
通信作者:姜向南 E-mail:13604412315@163.com
(收稿日期:2025-07-15 本文编辑:李晓乐)
更新日期/Last Update: 2026-03-20