Evidence map›Paper›PMID 26370104›Full record

ArticleDrug safety2015

Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection.

Alexandra C Pacurariu, Sabine M Straus, Gianluca Trifirò, Martijn J Schuemie, Rosa Gini, Ron Herings, Giampiero Mazzaglia, Gino Picelli, Lorenza Scotti, Lars Pedersen and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Drug safety, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 3 pooled it
3.2field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 66 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 8 institutions in 3 countries.

Alexandra C PacurariuDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Sabine M StrausDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Gianluca TrifiròDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Martijn J SchuemieDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Rosa GiniAgenzia Regionale di Sanità della Toscana, Florence, Italy.
Ron HeringsPHARMO Institute, Utrecht, The Netherlands.
Giampiero MazzagliaSocietà Italiana di Medicina Generale, Florence, Italy.
Gino PicelliPedianet-Società Servizi Telematici SRL, Padua, Italy.
Lorenza ScottiDepartment of Statistics, Università di Milano-Bicocca, Milan, Italy.
Lars PedersenDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
Peter ArlettEuropean Medicines Agency, London, UK.
Johan van der LeiDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Miriam C SturkenboomDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Preciosa M ColomaDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. p.coloma@erasmusmc.nl.
Erasmus MC · NLAarhus University Hospital · DKAgenzia Regionale di Sanità della Toscana · ITMedicines Evaluation Board · NLPharmo Institute · NLSocietà Italiana di Medicina Generale · ITUniversity of Messina · ITUniversity of Milano-Bicocca · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveSpontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data sources is essential to enable better detection, strengthening and validation of signals. In this study, we investigated the potential of electronic healthcare records (EHRs) to be used alongside an SRS as an independent system, with the aim of improving signal detection.

methodsA signal detection strategy, focused on a limited set of adverse events deemed important in pharmacovigilance, was performed retrospectively in two data sources-(1) the Exploring and Understanding Adverse Drug Reactions (EU-ADR) database network and (2) the EudraVigilance database-using data between 2000 and 2010. Five events were considered for analysis: (1) acute myocardial infarction (AMI); (2) bullous eruption; (3) hip fracture; (4) acute pancreatitis; and (5) upper gastrointestinal bleeding (UGIB). Potential signals identified in each system were verified using the current published literature. The complementarity of the two systems to detect signals was expressed as the percentage of the unilaterally identified signals out of the total number of confirmed signals. As a proxy for the associated costs, the number of signals that needed to be reviewed to detect one true signal (number needed to detect [NND]) was calculated. The relationship between the background frequency of the events and the capability of each system to detect signals was also investigated.

resultsThe contribution of each system to signal detection appeared to be correlated with the background incidence of the events, being directly proportional to the incidence in EU-ADR and inversely proportional in EudraVigilance. EudraVigilance was particularly valuable in identifying bullous eruption and acute pancreatitis (71 and 42 % of signals were correctly identified from the total pool of known associations, respectively), while EU-ADR was most useful in identifying hip fractures (60 %). Both systems contributed reasonably well to identification of signals related to UGIB (45 % in EudraVigilance, 40 % in EU-ADR) but only fairly for signals related to AMI (25 % in EU-ADR, 20 % in EudraVigilance). The costs associated with detection of signals were variable across events; however, it was often more costly to detect safety signals in EU-ADR than in EudraVigilance (median NNDs: 7 versus 5).

conclusionAn EHR-based system may have additional value for signal detection, alongside already established systems, especially in the presence of adverse events with a high background incidence. While the SRS appeared to be more cost effective overall, for some events the costs associated with signal detection in the EHR might be justifiable.

Indexed as

Adverse Drug Reaction Reporting SystemsDatabases, FactualData MiningDrug-Related Side Effects and Adverse ReactionsElectronic Health RecordsEuropeHumansMedical Records Systems, ComputerizedPharmacovigilance

Identifiers

PMID26370104
PMCPMC4659852
OpenAlexW2184328676

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.