Chen Jiexin, Zheng Qiongbing, Lan Youmian, Li Meijing, Lin Ling
Department of Rheumatology and Immunology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, China.
Department of Rheumatology, Shantou University Medical College, Shantou, 515041, China.
Sci Rep. 2025 Jan 4;15(1):827. doi: 10.1038/s41598-024-83524-y.
Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, which is significant for improving patient prognosis. Based on the NHANES for the periods of 2011-2012, 2013-2014, and 2015-2016, the study involved 11,366 participants, of whom 1,434 reported a diagnosis of OA. LASSO regression, XGBoost algorithm, and RF algorithm were used to identify significant indicators, and a OA prediction nomogram was developed. The nomogram was evaluated by measuring the AUC, calibration curve, and DCA curve of training and validation sets. In this study, we identified 5 predictors from 19 variables, including age, gender, hypertension, BMI and caffeine intake, and developed an OA nomogram. In both the training and validation cohorts, the OA nomogram exhibited good diagnostic predictive performance (with AUCs of 0.804 and 0.814, respectively), good consistency and stability in calibration curve and high net benefit in DCA. The nomogram based on 5 variables demonstrates a high accuracy in predicting the diagnosis of OA, indicating that it is a convenient tool for clinicians to identify potential populations of OA.
开发一种用于骨关节炎（OA）的新型诊断预测模型，以评估个体患OA的可能性，对于及时识别OA的潜在人群至关重要。这有助于进一步诊断和干预，对改善患者预后具有重要意义。基于2011 - 2012年、2013 - 2014年和2015 - 2016年的美国国家健康与营养检查调查（NHANES），该研究纳入了11366名参与者，其中1434人报告被诊断为OA。使用LASSO回归、XGBoost算法和RF算法来识别显著指标，并开发了OA预测列线图。通过测量训练集和验证集的AUC、校准曲线和决策曲线分析（DCA）曲线来评估列线图。在本研究中，我们从19个变量中确定了5个预测因素，包括年龄、性别、高血压、体重指数（BMI）和咖啡因摄入量，并开发了一个OA列线图。在训练队列和验证队列中，OA列线图均表现出良好的诊断预测性能（AUC分别为0.804和0.814），在校准曲线中具有良好的一致性和稳定性，在DCA中具有较高的净效益。基于5个变量的列线图在预测OA诊断方面具有较高的准确性，表明它是临床医生识别OA潜在人群的便捷工具。
