Literature DB >> 21182150

Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project.

Preciosa M Coloma1, Martijn J Schuemie, Gianluca Trifirò, Rosa Gini, Ron Herings, Julia Hippisley-Cox, Giampiero Mazzaglia, Carlo Giaquinto, Giovanni Corrao, Lars Pedersen, Johan van der Lei, Miriam Sturkenboom.   

Abstract

PURPOSE: In this proof-of-concept paper we describe the framework, process, and preliminary results of combining data from European electronic healthcare record (EHR) databases for large-scale monitoring of drug safety.
METHODS: Aggregated demographic, clinical, and prescription data from eight databases in four countries (Denmark, Italy, Netherlands, the UK) were pooled using a distributed network approach by generation of common input data followed by local aggregation through custom-built software, Jerboa(©). Comparison of incidence rates of upper gastrointestinal bleeding (UGIB) and nonsteroidal anti-inflammatory drug (NSAID) utilization patterns were used to evaluate data harmonization and quality across databases. The known association of NSAIDs and UGIB was employed to demonstrate sensitivity of the system by comparing incidence rate ratios (IRRs) of UGIB during NSAID use to UGIB during all other person-time.
RESULTS: The study population for this analysis comprised 19,647,445 individuals corresponding to 59,929,690 person-years of follow-up. 39,967 incident cases of UGIB were identified during the study period. Crude incidence rates varied between 38.8 and 109.5/100,000 person-years, depending on country and type of database, while age-standardized rates ranged from 25.1 to 65.4/100,000 person-years. NSAID use patterns were similar for databases within the same country but heterogeneous among different countries. A statistically significant age- and gender-adjusted association between use of any NSAID and increased risk for UGIB was confirmed in all databases, IRR from 2.0 (95%CI:1.7-2.2) to 4.3 (95%CI: 4.1-4.5).
CONCLUSIONS: Combining data from EHR databases of different countries to identify drug-adverse event associations is feasible and can set the stage for changing and enlarging the scale for drug safety monitoring.
Copyright © 2010 John Wiley & Sons, Ltd.

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Year:  2010        PMID: 21182150     DOI: 10.1002/pds.2053

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  105 in total

1.  A signal detection method to detect adverse drug reactions using a parametric time-to-event model in simulated cohort data.

Authors:  Victoria R Cornelius; Odile Sauzet; Stephen J W Evans
Journal:  Drug Saf       Date:  2012-07-01       Impact factor: 5.606

2.  Text mining for adverse drug events: the promise, challenges, and state of the art.

Authors:  Rave Harpaz; Alison Callahan; Suzanne Tamang; Yen Low; David Odgers; Sam Finlayson; Kenneth Jung; Paea LePendu; Nigam H Shah
Journal:  Drug Saf       Date:  2014-10       Impact factor: 5.606

3.  Application of a self-controlled case series study to a database study in children.

Authors:  Hanae Ueyama; Shiro Hinotsu; Shiro Tanaka; Hisashi Urushihara; Masaki Nakamura; Yuji Nakamura; Koji Kawakami
Journal:  Drug Saf       Date:  2014-04       Impact factor: 5.606

4.  [Monitoring treatment with biologics in non-infectious uveitis].

Authors:  T Barisani-Asenbauer
Journal:  Ophthalmologe       Date:  2011-01       Impact factor: 1.059

5.  Increased risk of microscopic colitis with use of proton pump inhibitors and non-steroidal anti-inflammatory drugs.

Authors:  Gwen M C Masclee; Preciosa M Coloma; Ernst J Kuipers; Miriam C J M Sturkenboom
Journal:  Am J Gastroenterol       Date:  2015-04-28       Impact factor: 10.864

6.  Evaluating performance of electronic healthcare records and spontaneous reporting data in drug safety signal detection.

Authors:  Vaishali K Patadia; Martijn J Schuemie; Preciosa Coloma; Ron Herings; Johan van der Lei; Sabine Straus; Miriam Sturkenboom; Gianluca Trifirò
Journal:  Int J Clin Pharm       Date:  2014-12-09

7.  Common Models, Different Approaches.

Authors:  Joshua J Gagne
Journal:  Drug Saf       Date:  2015-08       Impact factor: 5.606

8.  Pediatric readmission classification using stacked regularized logistic regression models.

Authors:  Gregor Stiglic; Fei Wang; Adam Davey; Zoran Obradovic
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

9.  An evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance.

Authors:  Xiaofeng Zhou; Sundaresan Murugesan; Harshvinder Bhullar; Qing Liu; Bing Cai; Chuck Wentworth; Andrew Bate
Journal:  Drug Saf       Date:  2013-02       Impact factor: 5.606

10.  Prescribing pattern of glucose lowering drugs in the United Kingdom in the last decade: a focus on the effects of safety warnings about rosiglitazone.

Authors:  Ingrid Leal; Silvana A Romio; Martijn Schuemie; Alessandro Oteri; Miriam Sturkenboom; Gianluca Trifirò
Journal:  Br J Clin Pharmacol       Date:  2013-03       Impact factor: 4.335

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