在区域医疗保健数据库中检测药物安全信号,使用基于树的扫描统计数据,并与其他3种采矿方法进行比较
Li Hailong1,2,3, Zhao Houyu4,5, Lin Hongbo6
1Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu, China.
British journal of clinical pharmacology
|June 1, 2023
概括
树Scan有效地检测电子健康记录中的类他类药物相关不良事件 (AE),优于其他方法. 该工具通过识别药物的潜在风险来加强药物安全监测.
科学领域:
- 药物监督 药物监督 药物监督
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 电子医疗数据库对于市场后药物安全监测至关重要.
- 识别与他类药物相关的不良事件 (AE) 需要强大的分析方法.
研究的目的:
- 为了比较基于树的扫描统计 (TreeScan) 与其他检测他类药物相关副作用的方法的性能.
- 为了评估TreeScan在现实世界电子医疗保健数据库中的实用性.
主要方法:
- 分析了中国医疗保健数据库 (2010-2016年),通过ICD-10代码识别了他类药物使用者和AE.
- 树Scan被应用于检测AE信号,与原始队列研究,贝叶斯信任传播神经网络 (BCPNN) 和Gamma Poisson Shrinker (GPS) 相比,性能更好.
- 性能指标包括灵敏度,特异性,积极的预测值和接收器运行特征曲线 (AUC-ROC) 下的面积.
主要成果:
- 树扫描识别了29个潜在的AE信号,其中包括9个已知与他类药物相关的AE.
- 与其他方法相比,TreeScan在多个指标上表现优异,包括特异性 (82.3%) 和AUC-ROC (75.8%).
- 树扫描,BCPNN和GPS的灵敏度为69.2%,明显高于原始队列研究 (46%).
结论:
- 树Scan是一个非常有效的工具,用于检测电子医疗数据的不良事件.
- 在这种情况下,它的性能优于原始队列分析和其他统计方法 (BCPNN,GPS) 等传统方法.
- 树Scan 作为一个有价值的补充工具,用于加强药物安全监测系统.
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