University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Pharmacoepidemiology Team, UMR 1219, 146 Rue Léo Saignat, BP 36, 33000, Bordeaux Cedex, France.
CHU de Bordeaux, Pôle de santé publique, Service de pharmacologie médicale, 33000, Bordeaux, France.
Drug Saf. 2018 Apr;41(4):377-387. doi: 10.1007/s40264-017-0618-y.
Signal detection from healthcare databases is possible, but is not yet used for routine surveillance of drug safety. One challenge is to develop methods for selecting signals that should be assessed with priority.
The aim of this study was to develop an automated system combining safety signal detection and prioritization from healthcare databases and applicable to drugs used in chronic diseases.
Patients present in the French EGB healthcare database for at least 1 year between 2005 and 2015 were considered. Noninsulin glucose-lowering drugs (NIGLDs) were selected as a case study, and hospitalization data were used to select important medical events (IME). Signal detection was performed quarterly from 2008 to 2015 using sequence symmetry analysis. NIGLD/IME associations were screened if one or more exposed case was identified in the quarter, and three or more exposed cases were identified in the population at the date of screening. Detected signals were prioritized using the Longitudinal-SNIP (L-SNIP) algorithm based on strength (S), novelty (N), and potential impact of signal (I), and pattern of drug use (P). Signals scored in the top 10% were identified as of high priority. A reference set was built based on NIGLD summaries of product characteristics (SPCs) to compute the performance of the developed system.
A total of 815 associations were screened and 241 (29.6%) were detected as signals; among these, 58 (24.1%) were prioritized. The performance for signal detection was sensitivity = 47%; specificity = 80%; positive predictive value (PPV) 33%; negative predictive value = 82%. The use of the L-SNIP algorithm increased the early identification of positive controls, restricted to those mentioned in the SPCs after 2008: PPV = 100% versus PPV = 14% with its non-use. The system revealed a strong new signal with dipeptidylpeptidase-4 inhibitors and venous thromboembolism.
The developed system seems promising for the routine use of healthcare data for safety surveillance of drugs used in chronic diseases.
从医疗保健数据库中检测信号是可行的，但尚未用于药物安全性的常规监测。其中一个挑战是开发用于优先评估的信号选择方法。
本研究旨在开发一种自动系统，结合从医疗保健数据库中检测和优先处理药物安全信号，适用于慢性病中使用的药物。
考虑了 2005 年至 2015 年间在法国 EGB 医疗保健数据库中至少存在 1 年的患者。选择非胰岛素类降血糖药物（NIGLD）作为案例研究，并使用住院数据选择重要医疗事件（IME）。从 2008 年到 2015 年，每季度使用序列对称分析进行信号检测。如果在该季度识别出一个或多个暴露病例，并且在筛选日期在人群中识别出三个或更多暴露病例，则筛选 NIGLD/IME 关联。使用基于强度（S）、新颖性（N）和信号潜在影响（I）以及药物使用模式（P）的纵向 SNIP（L-SNIP）算法对检测到的信号进行优先级排序。得分在前 10%的信号被确定为高优先级。基于 NIGLD 产品特性总结（SPC）构建了参考集，以计算所开发系统的性能。
共筛选了 815 个关联，检测到 241 个（29.6%）作为信号；其中 58 个（24.1%）被优先排序。信号检测的性能为敏感性=47%；特异性=80%；阳性预测值（PPV）33%；阴性预测值=82%。使用 L-SNIP 算法可以更早地识别出阳性对照物，这些对照物仅限于 2008 年后 SPC 中提到的对照物：使用 L-SNIP 算法时的 PPV=100%，而不使用时的 PPV=14%。该系统揭示了一种新型的二肽基肽酶-4 抑制剂和静脉血栓栓塞的强烈信号。
所开发的系统似乎有望用于慢性病中使用的药物的常规医疗保健数据安全性监测。
