中国组织工程研究

中国组织工程研究 ›› 2024, Vol. 28 ›› Issue (36): 5793-5798.doi: 10.12307/2024.671

• 数字化骨科Digital orthopedics • 上一篇    下一篇

老年脑卒中患者髋部骨折危险因素预测模型的建立和验证

杜  丽1，2，马一鸣1，3，赵  辉1，2，崔桂云1，2，祖  洁1，2   

  1. 1徐州医科大学第一临床医学院，江苏省徐州市   221001；徐州医科大学附属医院，2神经内科，3脊柱外科，江苏省徐州市   221006
  • 收稿日期:2023-08-10 接受日期:2023-09-18 出版日期:2024-12-28 发布日期:2024-02-27
  • 通讯作者: 祖洁，博士，主任医师，硕士生导师，徐州医科大学第一临床医学院，江苏省徐州市；徐州医科大学附属医院神经内科，江苏省徐州市 221006
  • 作者简介:杜丽，女，1997年生，山东省临沂市人，汉族，徐州医科大学在读硕士，主要从事帕金森与运动障碍疾病相关方面的研究。
  • 基金资助:
    江苏省高校重点实验室开放课题项目(XZSYSKF2022039)，项目负责人：祖洁；江苏省重点研发计划(社会发展)项目(BE2021630)，项目负责人：崔桂云

Establishment and validation of a prediction model of hip fracture risk factors in elderly stroke patients

Du Li1, 2, Ma Yiming1, 3, Zhao Hui1, 2, Cui Guiyun1, 2, Zu Jie1, 2   

  1. 1First Clinical Medical College, Xuzhou Medical University, Xuzhou 221001, Jiangsu Province, China; 2Department of Neurology, 3Department of Spine Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, Jiangsu Province, China
  • Received:2023-08-10 Accepted:2023-09-18 Online:2024-12-28 Published:2024-02-27
  • Contact: Zu Jie, MD, Chief physician, Master’s supervisor, First Clinical Medical College, Xuzhou Medical University, Xuzhou 221001, Jiangsu Province, China; Department of Neurology, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, Jiangsu Province, China
  • About author:Du Li, Master candidate, First Clinical Medical College, Xuzhou Medical University, Xuzhou 221001, Jiangsu Province, China; Department of Neurology, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, Jiangsu Province, China
  • Supported by:
    Open Project of Key Laboratory of Colleges and Universities in Jiangsu Province, No. XZSYSKF2022039 (to ZJ); Key Research & Development Program (Social Development) of Jiangsu Province, No. BE2021630 (to CGY)

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摘要：


文题释义：

髋部骨折：指股骨近端骨折，主要包括股骨颈骨折及股骨转子间骨折，是中老年人常见的一类骨质疏松性骨折。随着中国人口老龄化的加剧，髋部骨折患者数量逐年上升，目前已成为影响老年人生活质量的主要疾病之一，具有高致残率、高致死率的特点。
列线图预测模型：构建多因素回归模型，根据模型中各个危险因素对结局变量的影响对每个危险因素结果量化并赋分，再将各个评分相加得到总分，最后计算出该个体结局事件的预测值并以列线图形式表现。列线图又称诺莫图，是一种图形描述，将复杂的回归方程转变为可视化图形，使预测模型结果更具有可读性，方便对患者进行评估。将该模型制作成网页，为医患提供更便捷的交互体验。


背景：脑卒中后骨折的预防非常重要，目前尚无预测脑卒中后发生髋部骨折的模型。

目的：探讨导致脑卒中患者发生髋部骨折的危险因素，并建立风险预测模型将风险可视化。
方法：选择2014年6月至2017年6月徐州医科大学附属医院收治的脑卒中患者439例，男107例，女332例，平均(71.38±9.74)岁，根据脑卒中后有无髋部骨折分为骨折组(n=35)和非骨折组(n=404)。采用单因素和多因素分析确定脑卒中后发生髋部骨折的危险因素。将数据随机分为训练集(70%)和测试集(30%)，基于多因素分析结果，建立预测髋部骨折发生风险列线图，并使用受试者工作特征曲线、校准曲线和决策曲线对其性能进行评价。开发一个网络计算器用于给临床医生提供更方便的交互体验。

结果与结论：①单因素分析显示，两组间跌倒次数、吸烟、高血压、糖皮质激素、脑卒中次数、简易智能精神状态检查量表、视力水平、美国国立卫生研究院卒中量表、Berg平衡量表、交谈时停止步行测试比较差异均有显著性意义(P < 0.05)；②多因素分析显示，跌倒次数[OR=17.104，95%CI(3.727-78.489)，P=0.000]、美国国立卫生研究院卒中量表 [OR=1.565，95%CI(1.193-2.052)，P=0.001]、交谈时停止步行测试 [OR=12.080，95%CI(2.398-60.851)，P=0.003]是与脑卒中后髋部骨折呈正相关的独立危险因素，骨密度[OR=0.155，95%CI(0.044-0.546)，P=0.012]和Berg平衡量表[OR=0.840，95%CI(0.739-0.954)，P=0.007]与脑卒中后髋部骨折呈负相关；③在训练集和测试集中，列线图的曲线下面积AUC值分别为0.956和0.907，校准曲线显示预测值与实际状态吻合度较高，决策曲线下面积分别为0.038和0.030；④结果显示，跌倒次数多、骨密度低、Berg平衡量表评分低、美国国立卫生研究院卒中量表评分高、交谈时停止步行测试阳性是脑卒中后髋部骨折的危险因素。在此基础上建立列线图预测模型，并开发了一个网络计算器(https://stroke.shinyapps.io/DynNomapp/)。

https://orcid.org/0009-0004-4939-7077 (杜丽) 

中国组织工程研究杂志出版内容重点：人工关节；骨植入物；脊柱；骨折；内固定；数字化骨科；组织工程

关键词: 脑卒中, 骨质疏松, 髋部骨折, 跌倒风险, 列线图, 预测模型

Abstract: BACKGROUND: Prevention of fractures after stroke is very important, and there are currently no models to predict the occurrence of hip fractures after stroke.
OBJECTIVE: To investigate the risk factors leading to hip fracture in stroke patients and to establish a risk prediction model to visualize this risk. 
METHODS: A total of 439 stroke patients were selected from the Affiliated Hospital of Xuzhou Medical University from June 2014 to June 2017, including 107 males and 332 females, with a mean age of (71.38±9.74) years. They were divided into fracture group (n=35) and non-fracture group (n=404) according to the presence or absence of hip fracture. Univariate and multivariate analyses were used to determine the risk factors for hip fracture after stroke. The data were randomly divided into training set (70%) and test set (30%). Nomogram predicting the risk of hip fracture occurrence was created based on the results of the multifactor analysis, and performance was evaluated using receiver operating characteristic curve, calibration curves, and decision curve analysis. A web calculator was created to facilitate a more convenient interactive experience for clinicians. 
RESULTS AND CONCLUSION: (1) Univariate analysis showed significant differences between the two groups in the number of falls, smoking, hypertension, glucocorticoids, number of strokes, Mini-Mental State Examination, visual acuity level, National Institute of Health Stroke Scale, Berg Balance Scale, and Stop Walking When Talking scale scores (P < 0.05). (2) Multivariate analysis showed that number of falls [OR=17.104, 95%CI (3.727-78.489), P=0.000], National Institute of Health Stroke Scale [OR=1.565, 95%CI(1.193-2.052), P=0.001], Stop Walking When Talking [OR=12.080, 95%CI(2.398-60.851), P=0.003] were independent risk factors positively associated with new hip fractures. Bone mineral density [OR=0.155, 95%CI(0.044-0.546), P=0.012] and Berg Balance Scale [OR=0.840, 95%CI(0.739-0.954), P=0.007] were negatively associated with new hip fractures after stroke. (3) The AUC values of nomogram were 0.956 and 0.907 in the training and test sets, respectively, and the calibration curves showed a high agreement between predicted and actual status with an area under the decision curve of 0.038 and 0.030, respectively. (4) These findings conclude that the number of falls, low bone mineral density, low Berg Balance Scale score, high National Institute of Health Stroke Scale score, and positive Stop Walking When Talking are risk factors for hip fracture after stroke. Based on this, a nomogram with high accuracy was developed and a web calculator (https://stroke.shinyapps.io/DynNomapp/) was created. 

Key words: stroke, osteoporosis, hip fracture, fall risk, nomogram, prediction model

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