| Objective:This article establishes a mathematical model based on the gender,age,occupational posture,smoking and drinking habits,height,weight,and imaging changes of the lumbar spine MRI(SRI)paraspinal muscle,and through multivariate analysis and regression analysis(LRA).To analyze the influence of the correlation between the above risk factors on low back pain and the weight of each factor on the back pain.Methods:1.A total of 1378 patients who underwent lumbar back pain and were diagnosed with low back pain(LBP)from February 2018 to November 2018 were enrolled.197 patients were enrolled according to inclusion criteria.2.Telephone follow-up was included in the patient,and the patient's gender,age,height,weight,occupation,smoking and drinking history were collected,and the body mass index(BMI)was calculated.Visual analog scale(VAS)was performed on the degree of low back pain in patients.3.All patients underwent lumbar MRI plain scan.The horizontal cross-sectional images of L3/4 and L4/5 intervertebral discs were selected on MRI T2-weighted images,and the psoas muscles,multi-cleft muscles and the patients were used with mapping software image J 1.52a.The cross-sectional area of the erector spinae and the degree of fat infiltration(the degree of fat infiltration is calculated as the gray value,taking the average of both sides)is measured.4.Enter the above data into the computer and pass the SPSS 20.0 database.Analyze the correlation between various factors and low back pain;and the correlation between various risk factors.Through logistic analysis and multiple linear regression analysis,the weight of each factor on the degree of low back pain was analyzed.5.According to the regression analysis,the regression equation equation between the higher weight factor and the low back pain score is obtained,and the low back pain score obtained by the equation is compared with the VAS score.Results:1 Low back pain and various factors:1.1 Correlation between low back pain and parasitic muscle cross-sectional area and fat infiltration:Iow back pain was negatively correlated with L3/4,L4/5 two-stage multi-fissure muscle and psoas muscle cross-sectional area(P<0.05);low back pain and L3/4,L4/5 two-stage fat infiltration degree was positively correlated(P<0.05).1.2 Correlation between low back pain and basic information:The degree of low back pain was positively correlated with age and BMI(P<0.05);the degree of low back pain was significant(P<0.05).1.3 Correlation between low back pain and lifestyle habits:low back pain was positively correlated with posture(P<0.05);low back pain was negatively correlated with alcohol consumption(P<0.05);low back pain and smoking were not statistically significant(P>0.05).1.4 Risk ratio ratio(OR)according to logic analysis:the weight of the factors affecting low back pain is the highest posture2 Cross-sectional area of paravertebral muscles and correlation with various factors2.1 Correlation between cross-sectional area and basic information:cross-sectional area was negatively correlated with age(P<0.05);males had larger cross-sectional area than female paraspinal muscles;cross-sectional area and BMI were not statistically significant(P>0.05).2.2 Correlation between cross-sectional area and living habits:cross-sectional area was negatively correlated with smoking(P<0.05);cross-sectional area of multi-cleft muscle and psoas muscle was positively correlated with drinking(P<0.05);cross-sectional area of multi-cleft muscle There was a negative correlation with poor posture(P<0.05).Conclusions:1.The patient's age,gender,BMI,alcohol consumption,posture,cross-sectional area of paraspinal muscle and fat infiltration are related to low back pain;according to the risk analysis,there is weight,which has the greatest impact on posture;2.The cross-sectional area of the paraspinal muscle was negatively correlated with the patient's age,smoking,and poor posture.Drinking was positively correlated;the female cross-sectional area was smaller than that of the male;3.The degree of parasitic muscle fat infiltration is positively correlated with BMI and age;female fat infiltration is heavy;4.There is no statistical difference between the quantitative scores of the risk factors and the VAS scores. |
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