A prediction model‐based algorithm for computer‐assisted database screening of adverse drug reactions in the Netherlands

Purpose

The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model‐based approach.

Methods

A logistic regression‐based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug‐ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations.

Results

A total of 25 026 unique drug‐ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734–0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%).

Conclusions

A prediction model‐based approach can be a useful tool to create priority‐based listings for signal detection in databases consisting of spontaneous ADRs.     

Development of an algorithm to identify pregnancy episodes and related outcomes in health care claims databases: An application to antiepileptic drug use in 4.9 million pregnant women in France

Purpose

Access to claims databases provides an opportunity to study medication use and safety during pregnancy. We developed an algorithm to identify pregnancy episodes in the French health care databases and applied it to study antiepileptic drug (AED) use during pregnancy between 2007 and 2014.

Methods

The algorithm searched the French health care databases for discharge diagnoses and medical procedures indicative of completion of a pregnancy. To differentiate claims associated with separate pregnancies, an interval of at least 28 weeks was required between 2 consecutive pregnancies resulting in a birth and 6 weeks for terminations of pregnancy. Pregnancy outcomes were categorized into live births, stillbirths, elective abortions, therapeutic abortions, spontaneous abortions, and ectopic pregnancies. Outcome dates and gestational ages were used to calculate pregnancy start dates.

Results

According to our algorithm, live birth was the most common pregnancy outcome (73.9%), followed by elective abortion (17.2%), spontaneous abortion (4.2%), ectopic pregnancy (1.1%), therapeutic abortion (1.0%), and stillbirth (0.4%). These results were globally consistent with French official data. Among 7 559 701 pregnancies starting between 2007 and 2014, corresponding to 4 900 139 women, 6.7 per 1000 pregnancies were exposed to an AED. The number of pregnancies exposed to older AEDs, comprising the most teratogenic AEDs, decreased throughout the study period (?69.4%), while the use of newer AEDs increased (+73.4%).

Conclusions

We have developed an algorithm that allows identification of a large number of pregnancies and all types of pregnancy outcomes. Pregnancy outcome and start dates were accurately identified, and maternal data could be linked to neonatal data.

     

Epidemiology of drug exposure and adverse drug reactions in two swiss departments of internal medicine

AIMS: To explore drug exposure, frequency of adverse drug reactions (ADRs), types of ADRs, predisposing risk factors and ADR-related excess hospital stay in medical inpatients. METHODS: Structured data regarding patient characteristics, 'events' (symptoms, laboratory results), diagnoses (ICD10) and drug therapy were collected using a computer-supported data entry system and an interface for data retrieval from electronic patient records. ADR data were collected by 'event monitoring' to minimize possible bias by the drug monitor. The causality of each event was assessed in relation to disease(s) and drug therapy. RESULTS: The analysis included 4331 (100%) hospitalizations. The median observation period was 8 days. The median number of different drugs administered per patient and day was 6 and varied between 4 (Q1 ) and 9 (Q3 ) different drugs in 50% of all hospital days. In 41% of all hospitalizations at least one disease-unrelated event could be possibly attributed to drug therapy. Clinically relevant ADRs occurred in 11% of all hospitalizations. In 3.3% of all hospitalizations ADRs were the cause of hospital admission. The incidence of possibly ADR-related deaths was 1.4. Factors predisposing for clinically relevant ADRs were female gender and polypharmacy. ADR-related excess hospital stay accounted for 8. 6% of hospital days. CONCLUSIONS: These data demonstrate the feasibility of the developed 'event monitoring' system for quantitative analysis of ADRs in medical inpatients. With increasing numbers of recorded patients the pharmacoepidemiological database provides a valuable tool to study specific questions regarding drug efficacy and safety in hospitalized patients.     

呼和浩特某三甲医院骨科门诊超说明书用药调查及相关不良事件分析

目的：分析呼和浩特某三级甲等医院骨科门诊超说明书用药情况,探讨其与临床不良反应/事件的关系,为超说明书用药管理提供参考依据。方法：采用回顾性分析方法,分层随机抽取该院2015年1-12月骨科门诊处方,分类统计超说明书用药类型,结合循证医学报道分析超说明书用药可行性。汇总分析因超说明书用药诱发的不良反应/事件,与同期上报的不良反应发生率相比较。结果：该院骨科门诊超说明书处方比例为9.88%。在用药类型方面以超剂量（疗程）用药（57.42%）最多;在用药科室方面以骨科专家门诊超说明书用药比例（44.26%）最高。所有超说明书用药中仅有8.97%（32/357）有循证医学证据。该院可追踪的因超说明书用药诱发药品不良事件13例次,发生比例为4.76%（13/273）。结论：骨科门诊超说明书用药现象普遍存在,相关各方面应引起重视并加以管理规范,以减少不良事件发生,促进临床合理用药。     

我院药品不良反应/事件主动监测系统构建

目的:构建药品不良反应/事件主动监测系统,为临床提供用药安全信息,及时发现并快速上报药品不良反应(adverse drug reaction,ADR)/事件(adverse drug event,ADE),实现基于该系统的ADR/ADE真实世界研究条件.方法:联合“触发器原理”与“贝叶斯置信传播神经网络法”挖掘医院信息...