目的:非特异性下腰痛(NLBP)是一种常见的临床疾病,影响全球约60-80%的成年人。然而,目前在临床实践中缺乏科学的预测和评估系统。目的分析NLBP的危险因素,构建风险预测模型。
方法:我们收集了2020年12月至2022年12月在宁波市第六医院接受治疗的707例符合纳入标准的患者的基线数据。采用Logistic回归和LASSO回归筛选影响NLBP发病的独立危险因素,构建风险预测模型。通过十倍交叉验证评估模型的敏感性和特异性。并在验证集中执行内部验证。
结果:年龄,性别,BMI,教育水平,婚姻状况,锻炼频率,腰痛的病史,劳动强度,工作姿势,暴露于振动源,发现心理状态与NLBP的发作显着相关。使用这11个预测因素,构造了一个列线图,训练集的ROC曲线下面积为0.835(95%CI0.756-0.914),敏感性为0.771,特异性为0.800。验证组的ROC曲线下面积为0.762(95%CI0.665-0.858),敏感性为0.800,特异性为0.600,表明该模型对NLBP诊断的预测价值较高。此外,校准曲线显示预测和实际生存概率之间的高度一致性.
结论:我们建立了NLBP的初步预测模型,并构建了预测NLBP发病的列线图。该模型表现出良好的性能,可能对临床实践中NLBP的预防和治疗有用。
OBJECTIVE: Non-specific low back pain (NLBP) is a common clinical condition that affects approximately 60-80% of adults worldwide. However, there is currently a lack of scientific prediction and evaluation systems in clinical practice. The purpose of this
study was to analyze the risk factors of NLBP and construct a risk prediction model.
METHODS: We collected baseline data from 707 patients who met the inclusion criteria and were treated at the Sixth Hospital of Ningbo from December 2020 to December 2022. Logistic regression and LASSO regression were used to screen independent risk factors that influence the onset of NLBP and to construct a risk prediction model. The sensitivity and specificity of the model were evaluated by tenfold cross-validation, and internal validation was performed in the validation set.
RESULTS: Age, gender, BMI, education level, marital status, exercise frequency, history of low back pain, labor intensity, working posture, exposure to vibration sources, and psychological status were found to be significantly associated with the onset of NLBP. Using these 11 predictive factors, a nomogram was constructed, and the area under the ROC curve of the training set was 0.835 (95% CI 0.756-0.914), with a sensitivity of 0.771 and a specificity of 0.800. The area under the ROC curve of the validation set was 0.762 (95% CI 0.665-0.858), with a sensitivity of 0.800 and a specificity of 0.600, indicating that the predictive value of the model for the diagnosis of NLBP was high. In addition, the calibration curve showed a high degree of consistency between the predicted and actual survival probabilities.
CONCLUSIONS: We have developed a preliminary predictive model for NLBP and constructed a nomogram to predict the onset of NLBP. The model demonstrated good performance and may be useful for the prevention and treatment of NLBP in clinical practice.