首都医科大学学报 ›› 2026, Vol. 47 ›› Issue (3): 612-620.doi: 10.3969/j.issn.1006-7795.2026.03.025

• 临床研究 • 上一篇    下一篇

老年髋部骨折术后对侧髋部再骨折的危险因素及预测模型构建

许瀚驰,龙安华,王雪飞*,刘亮   

  1. 首都医科大学附属北京潞河医院创伤骨科,北京 101100
  • 收稿日期:2025-12-22 修回日期:2026-03-04 出版日期:2026-06-21 发布日期:2026-06-26
  • 通讯作者: 王雪飞 E-mail:wangxuefei@ccmu.edu.cn
  • 基金资助:
    首都卫生发展科研专项基金项目(2020-2-7081)。

Risk factors and prediction model for contralateral hip fracture in elderly patients after hip fracture surgery

Xu Hanchi, Long Anhua, Wang Xuefei* , Liu Liang   

  1. Department of Traumatic Orthopedics, Beijing Luhe Hospital, Capital Medical University, Beijing 101100, China
  • Received:2025-12-22 Revised:2026-03-04 Online:2026-06-21 Published:2026-06-26
  • Supported by:
    This study was supported by the Capital's Funds for Health Improvement and Research (2020-2-7081).

摘要: 目的  探讨髋部再骨折的危险因素,并构建列线图临床预测模型。方法  回顾性纳入2016年1月至2022年12月在首都医科大学附属北京潞河医院骨中心住院治疗的1 871例老年髋部骨折患者为研究对象。将术后3年内发生对侧髋部再骨折的176例患者作为病例组,未发生对侧髋部再骨折的1 695例患者为对照组。收集患者人口学资料、合并症及实验室检查指标,通过最小绝对收缩和选择算法(least absolute shrinkage and selection operator,LASSO)及多因素Logistic回归筛选老年髋部再骨折的危险因素,基于R语言构建列线图临床预测模型。通过受试者工作特征(receiver operating character,ROC)曲线,Hosmer-Lemeshow拟合优度检验,决策曲线分析(decision curve analysis,DCA)对模型进行评价。结果  最终9.4%(176/1 871)的患者术后3年内发生对侧髋部再骨折。多因素Logistics回归分析显示,年龄、糖尿病病史、其他部位骨质疏松性骨折病史、周围血管疾病、视觉损害和血小板计数是老年髋部再骨折的危险因素(P<0.05)。ROC曲线显示,列线图预测模型曲线下面积(area under the curve,AUC)=0.729(95%CI:0.685~0.773)。拟合优度检验提示预测与实际风险一致性良好,DCA曲线证实模型具有临床净获益。结论  年龄、糖尿病病史、其他部位骨质疏松性骨折病史、周围血管疾病、视觉损害和血小板计数是老年髋部再骨折的危险因素;基于各危险因素构建的预测列线图模型具有一定的预测效能,但预测效果一般,可为临床医生在初次髋部骨折术后的围手术期处理和治疗方案制定提供参考。

关键词: 髋部骨折, 对侧髋部骨折, 老年人, 危险因素, 风险评估, 列线图

Abstract: Objective  To identify the risk factors associated with contralateral hip fracture and to develop a nomogram-based clinical prediction model. Methods  A retrospective study was conducted including 1 871 elderly patients hospitalized for hip fracture at the bone center of a tertiary hospital in Beijing, China, between January 2016 and December 2022. Patients who developed a contralateral hip fracture within 3 years after surgery (n=76) were assigned to the case group, while those without contralateral hip fracture (n=1 695) as the control group. Demographic characteristics, comorbidities, and laboratory findings were collected. Least absolute shrinkage and selection operator (LASSO) regression followed by multivariate Logistic regression analyses were used to identify the risk factors for contralateral hip fracture, and a nomogram-based prediction model was developed by using R software. The model was evaluated with the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA). Results  Overall, 9.4% (176/1 871) of patients experienced a contralateral hip fracture within 3 years after surgery. Multivariate Logistic regression analysis identified age, history of diabetes mellitus, history of osteoporotic fractures at other sites, peripheral vascular disease, visual impairment, and platelet count as independent risk factors for contralateral hip fracture in older patients (P < 0.05). The ROC analysis indicated that the nomogram yield an area under the curve (AUC) of 0.729 (95% CI: 0.685-0.773). The Hosmer-Lemeshow test indicated good agreement between predicted and observed risks, and DCA confirmed that the model provided a net clinical benefit. Conclusion  Age, history of diabetes mellitus, history of osteoporotic fractures at other sites, peripheral vascular disease, visual impairment, and platelet count are independent risk factors for contralateral hip fracture in older patients. The nomogram incorporating these variables exhibits moderate predictive performance and may serve as a useful tool to assist clinicians in perioperative management and treatment decision-making following initial hip fracture surgery.

Key words: hip fractures, contralateral hip fracture, aged, risk factors, risk assessment, nomograms

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