Predicting the risk of non-specific low back pain in the young population: development and assessment of a new predictive nomogram.

OBJECTIVE: Non-specific low back pain is a common disorder that affects more than 80% of the world’s population. But the potential risk factors remain unclear. The aim of this study is to develop a nomogram for the risk prediction of low back pain in young population. PATIENTS AND METHODS: A total of 264 young participants (18-45 years old) were recruited and randomly divided into a training set (n=188) and a validation set (n=76) by a ratio of 7:3. The nomogram was developed based on the training set. The independent predictors of low back pain were identified by LASSO and logistic regression analysis. A nomogram was developed according to the predictors. To assess the reliability of the nomogram, the area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were applied. The validation set was used to validate the results. RESULTS: Sixteen factors were included in the characteristics of the eligible subjects. LASSO showed that five independent predictors including working posture, exercising hours per week, Tuffier’s line, six lumbar vertebrae anomaly, and lumbar lordosis angle were the independent risk factors of low back pain in young population, which were identified by multivariate logistic regression analysis and were used to establish the nomogram. The AUC values of the nomogram were 0.867 (95% CI: 0.809-0.924) and 0.868 (95% CI: 0.775-0.961) in the training and validation set, respectively. The calibration curve revealed that the prediction model of the nomogram was greatly consistent with the actual observation. In addition, the DCA indicated that the nomogram was clinically useful. CONCLUSIONS: Working posture, exercising hours per week, Tuffier’s line, six lumbar vertebrae anomaly, and lumbar lordosis angle are identified as independent predictors of non-specific low back pain in young population. And the nomogram based on the above five predictors can accurately predict the risk of low back pain in young people. [ABSTRACT FROM AUTHOR]