中国组织工程研究 ›› 2023, Vol. 27 ›› Issue (31): 4937-4942.doi: 10.12307/2023.537

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

中国中老年人腰痛相关因素分析及列线图预测模型的构建

朱洪柳,王  维   

  1. 锦州医科大学附属第一医院,辽宁省锦州市  121000
  • 收稿日期:2022-07-09 接受日期:2022-08-29 出版日期:2023-11-08 发布日期:2023-01-30
  • 通讯作者: 王维,硕士,副教授,硕士生导师,锦州医科大学附属第一医院,辽宁省锦州市 121000
  • 作者简介:朱洪柳,女,1997年生,江苏省南通市人,汉族,锦州医科大学在读硕士,主要从事康复医学与理疗学方面研究。

Correlation analysis of low back pain in middle-aged and elderly people in China and construction of a linear graph prediction mode

Zhu Hongliu, Wang Wei   

  1. The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, Liaoning Province, China
  • Received:2022-07-09 Accepted:2022-08-29 Online:2023-11-08 Published:2023-01-30
  • Contact: Wang Wei, Master, Associate professor, Master’s supervisor, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, Liaoning Province, China
  • About author:Zhu Hongliu, Master candidate, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, Liaoning Province, China

摘要:


文题释义:

腰痛:是临床上的常见病、多发病,现已上升成为全球范围内导致残疾的首要原因。腰痛不仅对患者的身心造成极大的痛苦,同时还会带来巨大的经济负担,如何有效减少腰痛的发生值得思考。
列线图预测模型:是利用参数/半参数/非参数的数学模型估计研究对象当前患有某种疾病的概率或将来发生某种结局的可能性,其作用可以体现在疾病三级预防中任何一个环节。

背景:目前针对腰痛的治疗方法繁多,但多数难以从根本上解决问题,如何有效降低腰痛的发生率值得临床工作者思考。             
目的:探讨国内中老年人腰痛发生的影响因素并构建腰痛的列线图预测模型,根据腰痛发生概率的高低,指导其进行不同强度、不同频率的运动锻炼和康复理疗,降低腰痛发生率。 
方法:采用中国健康与养老追踪调查2015年调查数据,以是否发生腰痛作为因变量,以纳入对象的年龄、性别、婚姻、居住地、锻炼、受教育水平、吸烟、饮酒、抑郁症状、体质量指数、睡眠时长、左手握力、右手握力、腰围共14个变量作为自变量,分析中老年人腰痛发生的独立影响因素,构建列线图预测模型,绘制模型的校准曲线、受试者工作特征曲线、计算c指数评估预测模型的区分度和校准度。

结果与结论:①共筛选出中老年人6 059例,将发生腰痛者作为患病组(n=1 263),不发生腰痛者作为对照组(n=4 796),构建原始数据集,以7∶3将原始数据集分为训练集(n=4 243)和验证集(n=1 816);②根据多因素logistics回归分析结果筛选出受教育水平、抑郁症状、睡眠时长、右手握力共4个变量构建列线图预测模型,模型的受试者工作特征曲线下面积为0.726,校准曲线拟合良好,模型的内部验证采用bootstrops法生成的校准曲线拟合良好,模型的外部验证采用验证集进行,外部验证的受试者工作特征曲线下面积为0.740,校准图拟合良好,说明模型具有较好的区分度和校准度;③提示根据受教育水平、抑郁症状、睡眠时长、右手握力等因素可以通过列线图预测模型预测中老年人腰痛发生的概率,及早进行预防,提高中老年人生活质量。

https://orcid.org/0000-0002-5574-0861(朱洪柳)

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: 中老年人, 腰痛, 列线图预测模型, 中国养老与健康追踪调查, 运动锻炼

Abstract: BACKGROUND: At present, there are many treatment methods for low back pain; however, most of them cannot fundamentally solve the problem. How to effectively reduce the incidence of low back pain is worth thinking about by clinical workers.
OBJECTIVE: To explore the influencing factors of low back pain in middle-aged and elderly people in China, construct a nomogram prediction model for low back pain and guide exercise and rehabilitation therapy with different intensities and frequencies according to the occurrence probability of low back pain to reduce the incidence of low back pain.
METHODS: The follow-up data from the China Health and Retirement Longitudinal Study (CHARLS) in 2015 were used to determine whether low back pain occurred or not. Fourteen variables, including age, sex, marriage, place of residence, exercise, education level, smoking, alcohol consumption, depressive symptoms, body mass index, sleep duration, left hand muscle strength, right hand muscle strength and waist circumference, were used as independent variables to analyze the independent influencing factors of the occurrence of low back pain in middle-aged and elderly people and construct the nomogram prediction model. The calibration curve and receiver operating characteristic curve of the model were drawn, and the C index was calculated to evaluate the discrimination and calibration degree of the prediction model. 
RESULTS AND CONCLUSION: (1) A total of 6 059 middle-aged and elderly patients were selected. Patients with low back pain were selected as the disease group (n=1 263), and those without low back pain were selected as the control group (n=4 796). The original data set was constructed. The original data set was divided into training set (n=4 243) and verification set (n=1 816) at a ratio of 7:3. (2) According to the results of multivariate logistics regression analysis, four variables, including education level, depressive symptoms, sleep duration and right hand muscle strength, were identified to construct the nomogram prediction model, and the area under the receiver operating characteristic curve of the model was 0.726. The calibration curve fit was good, and the calibration curve generated by bootstrops method was good for the internal verification of the model. The validation set was used for the external verification. The area under the receiver operating characteristic curve of external verification was 0.740 and the calibration graph fit was good, indicating that the model had good discrimination and calibration degree. (3) According to the factors such as education level, depressive symptoms, sleep duration, and right hand grip strength, the probability of occurrence of low back pain can be predicted by using the nomogram prediction model, and early prevention can improve the quality of life of middle-aged and elderly people.

Key words: middle-aged and elderly people, low back pain, nomogram prediction model, China Health and Retirement Longitudinal Study, exercise training

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