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一个模型来预测未来的生物或有针对性的合成DMARD切换在随后的临床访问在类风湿关节炎
Laura C Cappelli1, George Reed2, Dimitrios A Pappas3
1Division of Rheumatology, Johns Hopkins School of Medicine, 5501 Hopkins Bayview Circle, Suite 1B1, Baltimore, MD, 21224, USA. lcappel1@jhmi.edu.
Rheumatology and therapy
|October 19, 2023
概括
一个新的模型使用临床数据预测类风湿性关节炎 (RA) 生物开关. 诸如疾病活性和先前治疗等因素有助于估计在未来访问时切换生物药物的可能性.
科学领域:
- 类风湿病学 类风湿病学
- 临床预测建模模型
- 医疗信息学 医疗信息学
背景情况:
- 了解影响类风湿性关节炎 (RA) 生物开关的因素对于患者管理至关重要.
- 使用常规收集的临床数据开发预测模型可以帮助预测治疗变化.
研究的目的:
- 开发和验证RA患者生物切换的预测模型.
- 确定与转换生物制剂的可能性相关的关键临床因素.
主要方法:
- 使用来自CorEvitas RA注册表的数据开发了一个预测模型.
- 使用了机器学习技术,包括最佳子集回归,拉索和弹性网.
- 使用ROC曲线下的面积 (AUC) 评估模型的性能.
主要成果:
- 最终的模型包括先前的临床疾病活动指数 (CDAI) 类别,患者的疼痛,CDAI的变化,年龄组和先前的生物药物的数量.
- 该模型在训练组中实现了0.690的AUC,在测试组中达到0.687.
- 这些因素与转换生物药物的可能性有显著的关联.
结论:
- 创建了一个简单的模型来预测RA患者在下一次诊所访问时的生物切换.
- 这个模型可以帮助临床医生做出有关患者治疗轨迹的明智决策.
- 该工具为患者需要新生物治疗的可能性提供了有价值的见解.
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