用于预测类风湿关节炎患者药物治疗不依从风险的列线图的开发。
Development of a nomogram to predict medication nonadherence risk in patients with rheumatoid arthritis.
作者信息
Liu Zige, Ge Rui, Yang Tianxiang, Zhang Jinning, Zhang Bowen, Zhang Chen, Song Guorui, Chen Desheng
机构信息
School of Clinical Medicine, Guangxi Medical University Nanning 530000, Guangxi, China.
Department of Radiology, Rich Hospital of Nantong University Nantong 226000, Jiangsu, China.
出版信息
Am J Transl Res. 2022 Dec 15;14(12):9057-9065. eCollection 2022.
OBJECTIVES
Poor adherence among patients with chronic diseases including inflammatory rheumatic diseases (IRDs) is a complex and serious global health care problem. This study aimed to develop an intelligent nomogram using retrospectively collected patient clinical data for predicting nonadherence to biologic treatment in rheumatoid arthritis (RA) patients.
METHODS
The clinical characteristics of 102 RA patients were collected from outpatients and inpatients at the Orthopedic Departments of Ningxia General Hospital of Ningxia Medical University and Ningxia Hui Autonomous Region People's Hospital from October 2020 to September 2021. Adherence was evaluated using the proportion of treatment days covered within 6 months as the outcome event. A least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify risk predictors, and then multivariate logistic regression analysis was applied to construct the risk prediction model. Furthermore, the nomogram was plotted by multivariable logistic regression.
RESULTS
Among the 102 patients analyzed, 43 patients did not adhere to biologic therapy for various reasons. LASSO regression analysis identified age, sex, education level, disease activity, monthly income, medical insurance, and adverse drug reactions as the significant risk predictors. By incorporating these factors, the nomogram was plotted which showed good discrimination, calibration, and clinical value. The C-index was 0.759 (95% CI: 0.665-0.853), and the area under the receiver operating characteristic (ROC) curve was 0.7416 with a good calibration ability. Decision curve analysis showed that the prediction effect of this model could benefit about 75% of the patients without compromising the interests of other patients.
CONCLUSIONS
This nomogram could help medical staff identify patients with higher risk of nonadherence early, so that intervention measures can be taken in time.
目的
包括炎性风湿性疾病（IRDs）在内的慢性病患者依从性差是一个复杂且严重的全球医疗保健问题。本研究旨在利用回顾性收集的患者临床数据开发一种智能列线图，以预测类风湿关节炎（RA）患者对生物治疗的不依从性。
方法
收集了2020年10月至2021年9月在宁夏医科大学总医院骨科和宁夏回族自治区人民医院门诊及住院的102例RA患者的临床特征。以6个月内治疗天数的覆盖比例作为结局事件来评估依从性。采用最小绝对收缩和选择算子（LASSO）回归分析来识别风险预测因素，然后应用多变量逻辑回归分析构建风险预测模型。此外，通过多变量逻辑回归绘制列线图。
结果
在分析的102例患者中，43例患者因各种原因未坚持生物治疗。LASSO回归分析确定年龄、性别、教育程度、疾病活动度、月收入、医疗保险和药物不良反应为显著风险预测因素。通过纳入这些因素，绘制了列线图，其显示出良好的区分度、校准度和临床价值。C指数为0.759（95%CI：0.665 - 0.853），受试者操作特征（ROC）曲线下面积为0.7416，校准能力良好。决策曲线分析表明，该模型的预测效果可使约75%的患者受益，同时不损害其他患者的利益。
结论
该列线图可帮助医护人员早期识别不依从风险较高的患者，以便及时采取干预措施。
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