Evidence map›Paper›PMID 22861670›Full record

ArticleDrug safety2012

Methods for retrospective detection of drug safety signals and adverse events in electronic general practice records.

Andrew Tomlin, David Reith, Susan Dovey, Murray Tilyard

Abstract read
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In one paragraph

Article in Drug safety, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

4 authors.

Andrew TomlinBest Practice Advocacy Centre, Dunedin, New Zealand. andy.tomlin@bpac.org.nz
David Reith
Susan Dovey
Murray Tilyard

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExamination of clinical data routinely recorded in general practice provides significant opportunities for identifying and quantifying medicine-related adverse events not captured by spontaneous adverse reaction reporting systems. Robust pharmacovigilance methods for detecting and monitoring adverse events due to treatment with new and existing medicines are required to estimate the true extent of adverse events experienced by primary care patients.

objectivesThe aim of the study was to examine evidence of adverse events contained in general practice electronic records and to study observed events related to selective serotonin reuptake inhibitors (SSRIs) as an example of drug-specific pharmaceutical surveillance achievable with these data.

methodsElectronic clinical records for a cohort of 338 931 patients consulting from 2002 to 2007 were extracted from the patient management systems of 30 primary care clinics in New Zealand. Medical warnings files, prescription records and free text consultation notes were used to identify physician-recorded treatment cautions, including adverse events and medicines they were associated with. A structured chronological analysis of prescriptions, consultation notes and adverse events relating to patients prescribed the SSRI citalopram was undertaken, and included investigating reasons for switching treatment to another SSRI (fluoxetine or paroxetine) as a method for detecting evidence of drug safety signals. We compared the number of adverse events identified for patients at one practice with the number spontaneously reported to New Zealand's Centre for Adverse Reactions Monitoring (CARM).

resultsDuring the 6-year study period, 173 478 patients received 4 811 561 prescriptions. There were 37 397 allergies, adverse events and other warnings recorded for 24 994 patients (7.4%); adverse events relating to 65 different types of drug were reported. Medicines most frequently implicated in adverse event reports were antibacterials, analgesics, antihypertensive medicines, lipid-modifying agents and skin preparations. Citalopram was prescribed for 5612 patients, and 701 adverse events relating to citalopram were identified in the electronic health records of 473 (8.4%) patients. A total of 713 (12.7%) patients changed treatment from citalopram to another SSRI, and 164 reasons for the switch were identified: suspected adverse drug effects for 129 (78.7%), lack of effect for 29 (17.7%) and patient preference for 6 (3.7%). The most common adverse events preceding the switch were anxiety, nausea and headaches. Of the 725 adverse events and medical warnings recorded at one practice, 21 (2.9%) were spontaneously reported to the CARM.

conclusionsRoutinely recorded general practice data provide a wealth of opportunities for monitoring drug safety signals and for other patient safety issues. Medical warning records and consultation notes contain a wealth of information on adverse events but structured search methodologies are often required to identify these.

Indexed as

Adverse Drug Reaction Reporting SystemsElectronic Health RecordsDrug MonitoringDrug-Related Side Effects and Adverse ReactionsFemaleGeneral PracticeHumansMaleNew ZealandPrimary Health CareProduct Surveillance, PostmarketingRetrospective StudiesSelective Serotonin Reuptake InhibitorsSelective Serotonin Reuptake Inhibitors

Identifiers

PMID22861670

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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.