摘要
目的:开发类风湿关节炎(rheumatoid arthritis,RA)患者用药不依从性临床预测模型。方法:基于2018年1月至2019年3月102例RA患者的训练数据集开发预测模型,使用6个月治疗覆盖天数比例为终点事件进行评估,采用LASSO回归模型用于优化药物不依从性风险模型的特征选择,应用多变量logistic回归分析建立包含LASSO回归模型中选择的特征的预测模型,使用C指数、校准图、ROC曲线和决策曲线分析来评估预测模型的预测能力、校准和临床实用性,并使用Bootstrap进行内部验证。结果:预测模型中的预测因素包括年龄、疾病活动度、教育水平、月收入及焦虑程度。该模型显示出良好的预测能力,C指数为0.897(95%CI:0.827~0.906),ROC曲线下面积(AUC)为0.8787939。在内部验证中,C指数可能达到0.888。决策曲线分析表明,在不损害其他患者的利益情况下,该模型的预测效果可以使得约85%的患者受益。结论:该临床预测模型有助于临床医护人员及早识别不依从性风险较高的患者,从而能够及时采取干预措施。
Objective:To develop a clinical prediction model for nonadherence to biologic treatment in rheumatoid arthritis patients.Methods:We developed a prediction model based on the training data of 102 rheumatoid arthritis patients,and the data were collected from January 2018 to March 2019.Adherence was evaluated using the proportion of treatment days covered within 6 months as the outcome event.The LASSO regression model was used to optimize feature selection for medication nonadherence risk model.Multivariate Logistic regression analysis was applied to build a predicting model incorporating the feature selected in LASSO regression model.Predictability,calibration,and clinical usefulness of the predicting model were assessed using the C-index,calibration plot,ROC curve,decision curve analysis and internal validation was assessed using Bootstrap.Results:Predictors in the prediction model included age,disease activity,education level,monthly income and anxiety degree.The model displayed good prediction ability;C index was 0.897(95%CI:0.827-0.906),and the area under the ROC curve was 0.8787939 with a good calibration ability.High C index of 0.888 was reached in internal verification.Decision curve analysis showed that the prediction effect of the model could benefit about 85%of the patients without harming the interests of other patients.Conclusion:The clinical prediction model could help clinical medical staff to identify patients with higher risk of nonadherence early,so that intervention measures can be taken in time.
作者
蔡旭
肖剑伟
程钒钒
赵敏
尹志华
郭粉莲
CAI Xu;XIAO Jianwei;CHENG Fanfan;ZHAO Min;YIN Zhihua;GUO Fenlian(Department of Rheumatology,Shenzhen Futian District Hospital for Rheumatic Diseases,Shenzhen Guangdong 518000,China)
出处
《临床与病理杂志》
CAS
2021年第9期2125-2132,共8页
Journal of Clinical and Pathological Research
基金
国家自然科学基金(81102266)
深圳市医疗卫生三名工程(SZSM201602087)
福田区卫生公益性科研项目(FTWS2020053)。
关键词
类风湿关节炎
临床预测模型
不依从性
生物制剂
R软件
rheumatoid arthritis
clinical prediction model
nonadherence
biologic agent
R software
