PURPOSE:Upper gastrointestinal bleeding (UGIB) is a severe and frequent drug-related event. In order to enable efficient drug safety alert generation in the French National Healthcare System database (SNDS), we assessed and calibrated empirically case-based designs to identify drug associated with UGIB risk. METHODS:All cases of UGIB were extracted from SNDS (2009-2014) using two definitions. Positive and negative drug controls were used to compare 196 self-controlled case series (SCCS), case-control (CC) and case-population (CP) design variants. Each variant was evaluated in a 1/10th population sample using area under the receiver operating curve (AUC) and mean square error (MSE). Parameters that had major impacts on results were identified through logistic regression. Optimal designs were replicated in the unsampled population. RESULTS:Using a specific UGIB definition, AUCs ranged from 0.64 to 0.80, 0.44 to 0.61 and 0.50 to 0.67, for SCCS, CC and CP, respectively. MSE ranged from 0.07 to 0.39, 0.83 to 1.33 and 1.96 to 4.6, respectively. Univariate regressions showed that high AUCs were achieved with SCCS with multiple drug adjustment and a 30-day risk window starting at exposure. The top-performing SCCS variant in the unsampled population yielded an AUC = 0.84 and MSE = 0.14, with 10/36 negative controls presenting significant estimates. CONCLUSIONS:SCCS adjusting for multiple drugs and using a 30-day risk window has the potential to generate UGIB-related alerts in the SNDS and hypotheses on its potential population impact. Negative control implementation highlighted that low systematic error was generated but that protopathic bias and confounding by indication remained unaddressed issues.

译文

目的:上消化道出血(UGIB)是一种严重且频繁的药物相关事件。为了在法国国家医疗保健系统数据库(SNDS)中实现高效的药物安全警报生成,我们评估并校准了基于经验的基于案例的设计,以识别与UGIB风险相关的药物。
方法:所有的UGIB病例均使用两个定义从SNDS(2009-2014)中提取。使用阳性和阴性药物对照来比较196个自控病例系列(SCCS),病例对照(CC)和病例人群(CP)设计变体。使用接收器工作曲线(AUC)和均方误差(MSE)下的面积,在人口样本的1/10中评估每个变体。通过逻辑回归确定对结果有重大影响的参数。最佳设计在未抽样人群中重复进行。
结果:使用特定的UGIB定义,SCCS,CC和CP的AUC分别为0.64至0.80、0.44至0.61和0.50至0.67。 MSE的范围分别为0.07至0.39、0.83至1.33和1.96至4.6。单因素回归表明,SCCS的高AUCs可以通过多种药物调整和从暴露开始的30天风险窗口来实现。未抽样人群中表现最好的SCCS变体的AUC = 0.84,MSE = 0.14,其中10/36个阴性对照的估计值显着。
结论:针对多种药物进行调整并使用30天风险窗的SCCS可能会在SNDS中产生与UGIB相关的警报,并假设其可能对人群产生影响。负控制措施的实施突显出产生了较低的系统误差,但原发病率偏倚和因指示而造成的混淆仍未解决。

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