Li Rongxia, Weintraub Eric, McNeil Michael M, Kulldorff Martin, Lewis Edwin M, Nelson Jennifer, Xu Stanley, Qian Lei, Klein Nicola P, Destefano Frank
Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
Pharmacoepidemiol Drug Saf. 2018 Apr;27(4):391-397. doi: 10.1002/pds.4397. Epub 2018 Feb 15.
The objective of our study was to conduct a data mining analysis to identify potential adverse events (AEs) following MENACWY-D using the tree-temporal scan statistic in the Vaccine Safety Datalink population and demonstrate the feasibility of this method in a large distributed safety data setting.
Traditional pharmacovigilance techniques used in vaccine safety are generally geared to detecting AEs based on pre-defined sets of conditions or diagnoses. Using a newly developed tree-temporal scan statistic data mining method, we performed a pilot study to evaluate the safety profile of the meningococcal conjugate vaccine Menactra® (MenACWY-D), screening thousands of potential AE diagnoses and diagnosis groupings. The study cohort included enrolled participants in the Vaccine Safety Datalink aged 11 to 18 years who had received MenACWY-D vaccination(s) between 2005 and 2014. The tree-temporal scan statistic was employed to identify statistical associations (signals) of AEs following MENACWY-D at a 0.05 level of significance, adjusted for multiple testing.
We detected signals for 2 groups of outcomes: diseases of the skin and subcutaneous tissue, fever, and urticaria. Both groups are known AEs following MENACWY-D vaccination. We also identified a statistical signal for pleurisy, but further examination suggested it was likely a false signal. No new MENACWY-D safety concerns were raised.
As a pilot study, we demonstrated that the tree-temporal scan statistic data mining method can be successfully applied to screen broadly for a wide range of vaccine-AE associations within a large health care data network.
我们研究的目的是进行数据挖掘分析，利用疫苗安全数据链人群中的树状时间扫描统计量来识别接种MenACWY-D疫苗后潜在的不良事件（AE），并证明该方法在大型分布式安全数据环境中的可行性。
疫苗安全性方面使用的传统药物警戒技术通常旨在根据预先定义的条件或诊断集来检测不良事件。我们使用一种新开发的树状时间扫描统计量数据挖掘方法进行了一项试点研究，以评估四价脑膜炎球菌结合疫苗Menactra®（MenACWY-D）的安全性，筛查了数千种潜在的AE诊断和诊断分组。研究队列包括2005年至2014年间在疫苗安全数据链中登记的11至18岁且接种过MenACWY-D疫苗的参与者。采用树状时间扫描统计量在0.05的显著性水平下识别接种MenACWY-D疫苗后不良事件的统计关联（信号），并对多重检验进行了校正。
我们检测到两组结果的信号：皮肤和皮下组织疾病、发热和荨麻疹。这两组都是接种MenACWY-D疫苗后已知的不良事件。我们还识别出胸膜炎的一个统计信号，但进一步检查表明它可能是一个假信号。未发现新的MenACWY-D疫苗安全问题。
作为一项试点研究，我们证明了树状时间扫描统计量数据挖掘方法可以成功应用于在大型医疗数据网络中广泛筛查多种疫苗-不良事件关联。
