开发可互操作的可计算的表型算法,用于特别感兴趣的不良事件,用于生物制品安全监测:验证研究
Ashley A Holdefer1, Jeno Pizarro1, Patrick Saunders-Hastings2
1IBM Consulting, Bethesda, MD, United States.
JMIR public health and surveillance
|July 15, 2024
概括
这项研究开发了可计算的表型算法来监测疫苗安全性,以高精度识别特别感兴趣的不良事件 (AESI). 这些算法表现出强大的积极预测值,支持它们在生物制剂市场后监测中的使用.
科学领域:
- 药物监督和药物安全研究.
- 开发和验证用于健康监测的计算方法.
- 疫苗相关不良事件的流行病学.
背景情况:
- 接种疫苗后的不良事件,包括COVID-19疫苗,需要强有力的市场后监测.
- 美国食品和药物管理局 (FDA) 监测特别感兴趣的特定不良事件 (AESI),以确保疫苗的安全性.
- 现有的监测方法正在得到加强,以改善检测和评估潜在的疫苗相关不良事件.
研究的目的:
- 通过可计算的表型算法增强疫苗安全的积极监测能力.
- 确定五种特定的AESI (过敏反应,吉兰-巴雷综合征,心肌炎/心周炎,血栓与血栓塞缩综合征,发烧性) 针对COVID-19和其他疫苗.
- 评估这些算法的积极预测值 (PPV),以准确识别后生物不良事件.
主要方法:
- 开发了基于规则的可计算的表型算法,使用标准代码查询电子健康记录 (EHR) 数据.
- 算法基于已发表的病例定义和AESI症状,诊断和治疗的临床输入.
- 使用来自美国学术卫生系统的EHR数据验证了算法性能,临床医生对一组病例进行了审查.
主要成果:
- 过敏反应算法实现了最高的PPV,为93.3%.
- 其他监测的AESI的PPV包括发烧性发作 (89%),心肌炎/心周炎 (83.5%),以及带有血栓塞缩综合征的血栓形成 (70.2%).
- 吉兰-巴雷综合征算法的PPV为47.2%.
结论:
- 开发的算法显示出足够的积极预测价值,可以有效地对AESI进行市场后监测.
- 结果支持持续开发和实施互操作算法,以广泛检测不良事件.
- 这种方法有助于确保疫苗的安全性,同时考虑到数据隐私和成本效益考虑.
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