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首页> 外文期刊>Patient Preference and Adherence >Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population: development and assessment of a new predictive nomogram
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Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population: development and assessment of a new predictive nomogram

机译：预测中国炎性风湿病人群中药物非依从性的风险：开发和评估新的预测诺模图

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Purpose: The aim of this study was to develop and internally validate a medication nonadherence risk nomogram in a Chinese population of patients with inflammatory rheumatic diseases. Patients and methods: We developed a prediction model based on a training dataset of 244 IRD patients, and data were collected from March 2016 to May 2016. Adherence was evaluated using 19-item Compliance Questionnaire Rheumatology. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the medication nonadherence risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C -index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation. Results: Predictors contained in the prediction nomogram included use of glucocorticoid (GC), use of nonsteroidal anti-inflammatory drugs, number of medicine-related questions, education level, and the distance to hospital. The model displayed good discrimination with a C -index of 0.857 (95% confidence interval: 0.807–0.907) and good calibration. High C -index value of 0.847 could still be reached in the interval validation. Decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 14%. Conclusion: This novel nonadherence nomogram incorporating the use of GC, the use of nonsteroidal anti-inflammatory drugs, the number of medicine-related questions, education level, and distance to hospital could be conveniently used to facilitate the individual medication nonadherence risk prediction in IRD patients.
机译：目的：本研究的目的是开发和内部验证中国风湿性风湿病患者的药物非依从性风险诺模图。患者和方法：我们基于244名IRD患者的训练数据集开发了预测模型，并从2016年3月至2016年5月收集了数据。使用19个项目的风湿病依从性问卷评估了依从性。最小绝对收缩和选择算子回归模型用于优化药物不依从风险模型的特征选择。应用多变量logistic回归分析来建立预测模型，该模型结合了在最小绝对收缩率和选择算子回归模型中选择的特征。使用C指数，校准图和决策曲线分析评估了预测模型的区别，校准和临床实用性。内部验证使用自举验证进行评估。结果：预测列线图中包含的预测因素包括糖皮质激素（GC）的使用，非甾体类抗炎药的使用，与医学有关的问题的数量，受教育程度以及到医院的距离。该模型显示出良好的辨别力，其C指数为0.857（95％置信区间：0.807-0.907）和良好的校准。在间隔验证中仍可以达到0.847的高C指数值。决策曲线分析表明，当将不依从可能性阈值确定为14％时，不依从诺模图在临床上很有用。结论：这种新颖的非粘附性诺模图结合了GC的使用，非甾体类抗炎药的使用，与药物相关的问题的数量，受教育程度以及到医院的距离，可以方便地促进IRD中个体药物非粘附性风险的预测耐心。

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