Risk Factors Associated with Pain Severity in Patients with Non-specific Low Back Pain in Southern China

STUDY DESIGN: A prospective cross-sectional study. PURPOSE: To evaluate the risk factors associated with the severity of pain intensity in patients with non-specific low back pain (NSLBP) in Southern China. OVERVIEW OF LITERATURE: Low back pain (LBP) is the leading cause of activity limitation and work absence throughout the world, so a firm understanding of the risk factor associated with NSLBP can provide early and prompt interventions that are aimed at attaining long-term results. METHODS: Participants were recruited from January 2014 to January 2016 and were surveyed using a self-designed questionnaire. Anonymous assessments included Short Form 36-Item Health Survey (SF-36) and Visual Analogue Scale (VAS). The association between the severity of NSLBP and these potential risk factors were evaluated. RESULTS: A total of 1,046 NSLBP patients were enrolled. The patients with primary school education, high body mass index (BMI), those exposed to sustained durations of driving and sitting, smoking, recurrent LBP had increased VAS and Oswestry Disability Index (ODI) scores with lower SF-36 scores (p10 kg objects in a quarter of their work time for >10 years had higher VAS and ODI scores with lower SF-36 scores (p<0.01). Multiple logistic regression showed lower levels of education, LBP for 1–7 days, long-lasting LBP in last year, smoking, long duration driving, and higher BMI were associated with more severe VAS score. CONCLUSIONS: The severity of NSLBP is associated with lower levels of education, poor standards of living, heavy physical labor, long duration driving, and sedentary lifestyle. Patients with recurrent NSLBP have more severe pain. Reducing rates of obesity, the duration of heavy physical work, driving or riding, and attenuating the prevalence of sedentary lifestyles and smoking may reduce the prevalence of NSLBP.

Knowledge discovery in database and its application in clinical diagnosis / 生物医学工程学杂志

Nowadays the tremendous amount of data has far exceeded our human ability for comprehension, and this has been particularly true for the medical database. However, traditional statistical techniques are no longer adequate for analyzing this vast collection of data. Knowledge discovery in database and data mining play an important role in analyzing data and uncovering important data patterns. This paper briefly presents the concepts of knowledge discovery in database and data mining, then describes the rough set theory, and gives some examples based on rough set.