A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes.
A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes.
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DOI：
10.1002/edm2.237
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发表时间：
2021-07
影响因子：
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通讯作者：
Patorno E
中科院分区：
文献类型：
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作者：
Fralick M;Kulldorff M;Redelmeier D;Wang SV;Vine S;Schneeweiss S;Patorno E
临床试验通常不足以检测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，使用其上市后不久和公众意识到其潜在安全性问题之前的真实的世界数据，检测卡格列净（美国首个钠葡萄糖协同转运蛋白2（SGLT 2抑制剂））的潜在不良事件。在美国，S.在商业声明数据集（2013年3月29日至2015年9月30日）中，确定了两个18岁以上的2型糖尿病（T2D）患者成对队列，这些患者接受了新分发的卡格列净或活性对照药物，即二肽基肽酶4抑制剂（DPP 4）或胰高血糖素样肽1受体激动剂（GLP 1），并进行了倾向评分匹配。我们使用可变比率匹配，每个接受卡格列净的人最多有4人接受DPP4或GLP1。我们使用基于分层树的扫描统计数据挖掘方法识别潜在安全性信号，其中分层结局树是基于国际疾病分类编码构建的。在调整多次测试后，我们筛查了卡格列净与对照药物启动者之间观察到的结果比偶然预期的更多的不良事件。我们确定了两个成对倾向评分变量比率匹配队列，分别为44，733名卡格列净vs 99，458名DPP4启动者和55，974名卡格列净vs 74，727名GLP1启动者。当我们筛查住院和急诊室诊断时，糖尿病酮症酸中毒是唯一与卡格列净治疗相关的严重不良事件，两个队列中p <0.05。当门诊诊断也被考虑时，女性和男性生殖器感染的信号出现在两个队列中（p <0.05）。在一项基于大型人群的研究中，我们发现了与卡格列净相关的已知但未发现其他不良事件，这为卡格列净在成人T2D患者中的安全性提供了保证，并表明基于树的扫描统计方法是新批准药物的有用的上市后安全性监测工具。临床试验通常不足以检测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，使用卡格列净上市后不久和公众意识到其潜在安全性问题之前的真实的世界数据，检测卡格列净（美国第一种SGLT 2抑制剂）的潜在不良事件。在一项基于大型人群的研究中，我们发现了与卡格列净相关的已知但未发现其他不良事件，这为卡格列净在成人T2D患者中的安全性提供了保证，并表明基于树的扫描统计是新批准药物的有用的上市后安全性监测工具。
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. 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. 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). 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. 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 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. 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 is a useful post‐marketing safety monitoring tool for newly approved medications.