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
The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, Liaoning Province, China
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