背景:与疫苗接种相关的不良事件已经通过流行病学研究进行了评估,最近随着几种COVID-19疫苗的紧急使用授权而获得了更多关注。作为其进行上市后监督的责任的一部分,美国食品和药物管理局继续监测几个特别关注的不良事件(AESI),以确保疫苗的安全性,包括COVID-19。
目的:本研究是生物制品有效性和安全性倡议的一部分,其目的是提高食品和药物管理局的上市后监督能力,同时最大限度地减少公众负担。这项研究旨在通过以规则为基础的方法加强主动监测工作,可计算的表型算法,用于识别疾病控制和预防中心正在监测的5种AESI,用于COVID-19或其他疫苗:过敏反应,格林-巴利综合征,心肌炎/心包炎,血栓形成伴血小板减少综合征,和高热癫痫。这项研究检查了这些表型是否具有足够高的阳性预测值(PPV),以确保选择进行监测的病例合理地可能是后生物学不良事件。这允许患者隐私,在评估我们方法的成本效益时,可以适当考虑发生非不良事件的患者数据共享的安全性问题.
方法:开发了AESI表型算法,通过查询标准和可互操作的代码,将其应用于全国医疗服务提供者组织的电子健康记录数据。规则中查询的代码代表症状,诊断,或AESI的治疗来自已发布的病例定义和临床医生的输入。为了验证算法的性能,我们将其应用于来自美国学术卫生系统的电子健康记录数据,并提供了病例样本供临床医生评估.使用PPV评估性能。
结果:PPV为93.3%,我们的过敏反应算法表现最好.我们高热癫痫发作的PPV,心肌炎/心包炎,血小板减少综合征,格林-巴利综合征算法占89%,83.5%,70.2%,和47.2%,分别。
结论:鉴于我们的算法设计和性能,我们的研究结果支持继续研究使用互操作算法进行广泛的AESI上市后检测.
BACKGROUND: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19.
OBJECTIVE: This
study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration\'s postmarket surveillance capabilities while minimizing public burden. This
study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This
study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach.
METHODS: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV.
RESULTS: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively.
CONCLUSIONS: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection.