构建欧盟药品不良反应时间索引参考数据集，助力早期信号检测评估
Development of a European Union Time-Indexed Reference Dataset for Assessing the Performance of Signal Detection Methods in Pharmacovigilance using a Large Language Model
- 基于药品说明书更新时间构建时序标注数据集
- 覆盖1479种药品、11万条药物-不良反应关联，74.5%事件在上市前被识别
- 适合药监评估、信号检测方法对比及真实世界研究者使用
背景：信号检测方法的优化受限于缺乏可靠的参考数据集。现有数据无法反映不良事件（AE）被监管机构正式确认的时间，难以限定分析时段至确认前，限制了对早期检测性能的评估。本研究通过构建欧盟（EU）时间索引参考数据集，整合药品说明书（SmPC）中不良事件纳入时间与监管元数据，填补这一空白。方法：从欧盟药品注册数据库获取1,513种集中审批药品的当前及历史版说明书（数据截止日期：2025年12月15日），利用DeepSeek V3模型提取4.8章节中的不良事件，并程序化提取标签变更等监管信息。时间索引依据事件首次纳入说明书的日期。结果：共涵盖17,763个说明书版本（1995–2025年），包含125,026条药物-AE关联。在活跃产品中，时间索引数据集包含1,479种药品和110,823条关联。大多数事件在上市前被识别（74.5%），安全更新高峰出现在2012年左右。胃肠道、皮肤及神经系统疾病所属系统器官分类（SOC）最常见。每种药物平均关联48个不良反应，涉及14个系统器官分类。结论：该数据集通过引入不良事件的官方确认时间，为药物流行病学监测提供了关键基准，支持更精准的信号检测性能评估，并促进不同分析方法间的比较。
原文摘要 · Abstract (English)
Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata. Methods: Current and historical Summaries of Product Characteristics (SmPCs) for all centrally authorized products (n=1,513) were retrieved from the EU Union Register of Medicinal Products (data lock: 15 December 2025). Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs. Regulatory metadata, including labelling changes, were programmatically extracted. Time indexing was based on the date of AE inclusion in the SmPC. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. The time-indexed reference dataset, restricted to active products, included 1,479 medicinal products and 110,823 drug-AE associations. Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%). Safety updates peaked around 2012. Gastrointestinal, skin, and nervous system disorders were the most represented System Organ Classes. Drugs had a median of 48 AEs across 14 SOCs. Conclusions: The proposed dataset addresses a critical gap in pharmacovigilance by incorporating temporal information on AE recognition for the EU, supporting more accurate assessment of signal detection performance and facilitating methodological comparisons across analytical approaches.
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