Pfizer Inc, 235 East 42nd St., New York, NY, USA.
New York University School of Medicine, New York, NY, USA.
Drug Saf. 2018 Jun;41(6):565-577. doi: 10.1007/s40264-018-0640-8.
Signal detection remains a cornerstone activity of pharmacovigilance. Routine quantitative signal detection primarily focuses on screening of spontaneous reports. In striving to enhance quantitative signal detection capability further, other data streams are being considered for their potential contribution as sources of emerging signals, one of which is longitudinal observational databases, including electronic medical record (EMR) and transactional insurance claims databases. Quantitative signal detection on such databases is a nascent field-with published methods being primarily based either on individual metrics, which may not effectively represent the complexity of the longitudinal records and their necessary variation for analysis for drug-outcome pairs, or on visualization discovery approaches leveraging multiple aspects of the records, which are not particularly tractable to high-throughput hypothesis-free signal detection. One extensively tested example of the latter is chronographs.
We apply a disturbance detection algorithm to chronographs using UK EMR The Health Improvement Network (THIN) data. The algorithm utilizes autoregressive integrated moving average (ARIMA)-based time series methodology designed to find disturbances that occur outside the normal pattern trends of the ARIMA model for the chronograph. Chronographs currently highlight drug-event pairs as potentially worthy of further clinical assessment, via filter-based increases in disproportionality scores from before to after the index drug exposure, tested across a range of case and control windows.
We replicate the findings on six exemplar chronographs from a previous publication, and show how disturbances can be effectively detected across this set of pairs. Further, 692 disturbances were detected in analysis of all 384 individual READ code outcomes ever recorded 50 or more times for patients prescribed sibutramine. The disturbances are algorithmically further broken into subsets of clinical interest.
Overall, the disturbance algorithm approach shows promising capacity for detecting outliers, and shows tractability of the algorithmic approach for large-scale screening. The method offers an array of pattern types for detection and clinical review.
信号检测仍然是药物警戒的基石活动。常规的定量信号检测主要侧重于自发报告的筛选。为了进一步提高定量信号检测能力，正在考虑其他数据流，以挖掘潜在的新兴信号源，其中之一是纵向观察性数据库，包括电子病历（EMR）和交易性保险索赔数据库。此类数据库的定量信号检测尚处于起步阶段-已发表的方法主要基于单个指标，这些指标可能无法有效反映纵向记录的复杂性及其对药物-结果对进行分析的必要变化，或者基于利用记录多个方面的可视化发现方法，但这些方法不太适合高通量无假设信号检测。后者的一个经过广泛测试的示例是计时图。
我们使用英国电子病历健康改善网络（THIN）数据，使用干扰检测算法对计时图进行分析。该算法利用基于自回归综合移动平均（ARIMA）的时间序列方法学，旨在找到计时图的 ARIMA 模型正常趋势之外的干扰。计时图目前通过在指数药物暴露前后基于过滤的比例失调分数增加来突出显示潜在值得进一步临床评估的药物-事件对，在一系列病例和对照窗口中进行测试。
我们复制了先前出版物中六个范例计时图的发现，并展示了如何在这组对中有效地检测干扰。此外，在对所有 384 个为服用西布曲明的患者记录的 50 次或更多次的 READ 代码结果进行分析中，检测到 692 个干扰。这些干扰是通过算法进一步分为具有临床意义的子集。
总的来说，干扰算法方法显示出检测异常值的良好能力，并且算法方法具有大规模筛选的可操作性。该方法提供了一系列用于检测和临床审查的模式类型。
