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首页> 外文期刊>Endocrinology, Diabetes & Metabolism >A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes
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A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes

机译：一种新型数据挖掘应用，用于检测新批准的糖尿病患者常规护理中的新批准药物的安全信号

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Background Clinical trials are often underpowered to detect serious but rare adverse events of a new medication. We applied a novel data mining tool to detect potential adverse events of canagliflozin, the first sodium glucose co-transporter 2 (SGLT2 inhibitor) in the United States, using real-world data from shortly after its market entry and before public awareness of its potential safety concerns. Methods In a U. S. commercial claims dataset (29 March 2013–30 Sept 2015), two pairwise cohorts of patients over 18?years of age with type 2 diabetes (T2D) who were newly dispensed canagliflozin or an active comparator, that is a dipeptidyl peptidase 4 inhibitor (DPP4) or a glucagon-like peptide 1 receptor agonist (GLP1), were identified and propensity score-matched. We used variable ratio matching with up to four people receiving a DPP4 or GLP1 for each person receiving canagliflozin. We identified potential safety signals using a hierarchical tree-based scan statistic data mining method with the hierarchical outcome tree constructed based on international classification of disease coding. We screened for incident adverse events where there were more outcomes observed among canagliflozin vs. comparator initiators than expected by chance, after adjusting for multiple testing. Results We identified two pairwise propensity score variable ratio matched cohorts of 44,733 canagliflozin vs. 99,458 DPP4 initiators, and 55,974 canagliflozin vs. 74,727 GLP1 initiators. When we screened inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe adverse event associated with canagliflozin initiation with p ?.05 in both cohorts. When outpatient diagnoses were also considered, signals for female and male genital infections emerged in both cohorts ( p ?.05). Conclusions and relevance In a large population-based study, we identified known but no other adverse events associated with canagliflozin, providing reassurance on its safety among adult patients with T2D and suggesting the tree-based scan statistic method is a useful post-marketing safety monitoring tool for newly approved medications.
机译：背景技术临床试验往往受到新药的严重但罕见的不良事件。我们应用了一种新型数据挖掘工具，以检测美洲钠葡萄糖共转运蛋白2（SGLT2抑制剂）的潜在不良事件，在美国市场进入之后不久和公众意识到其潜力的潜力安全问题。方法在美国商业索赔数据集（2015年9月29日2015年9月29日）中，两种成对患者18岁以上的患者，其中2岁以上型糖尿病（T2D）是新分配的羊皮lozin或活性比较剂，即二肽基肽酶鉴定了4个抑制剂（DPP4）或胰高血糖素样肽1受体激动剂（GLP1），并达到分数匹配。我们使用可变比率与最多四人接受接受蜜饯的人接受DPP4或GLP1的人。我们使用基于分层树的扫描统计数据挖掘方法识别了具有基于国际疾病编码的国际分类的分层结果树的潜在安全信号。在调整多重测试后，我们筛选了在Canagliflozin与比较者中观察到的发生不良事件的发生不良事件。结果我们鉴定了两对成对倾升得分可变比匹配队列44,733蜜杆菌酶与99,458dpp4引发剂，55,974甲基三唑唑唑酶与74,727glp1引发剂。当我们筛选住院病人和急诊室诊断时，糖尿病酮酸病症是与蜜胶激素引发的唯一严重的不良事件，其与P＆β。在两个队列中。当也考虑外门诊诊断时，在两个坐骨中出现的女性和男性生殖器感染的信号（P <。05）。结论和相关性在大量的基于人口的研究中，我们确定了与蜜胶中有关的其他不良事件，在成人T2D患者中提供了对其安全性的保证，并提出基于树的扫描统计方法是一个有用的营销后安全监测新批准的药物工具。

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