Evidence map›Paper›PMID 26153397›Full record

Observational studyDrug safety2015

A Method to Combine Signals from Spontaneous Reporting Systems and Observational Healthcare Data to Detect Adverse Drug Reactions.

Ying Li, Patrick B Ryan, Ying Wei, Carol Friedman

Abstract readObservational Study
In one paragraph

Observational study 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.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–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

36 citing papers in PubMed.

  1. Article
  2. Generative Transformers for Pharmacovigilance Signal Detection using Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Ying LiDepartment of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, Presbyterian Building 20th Floor, New York, NY, 10032, USA. yl2565@columbia.edu.
Patrick B RyanDepartment of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, Presbyterian Building 20th Floor, New York, NY, 10032, USA.
Ying WeiDepartment of Biostatistics, Columbia University, New York, NY, 10032, USA.
Carol FriedmanDepartment of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, Presbyterian Building 20th Floor, New York, NY, 10032, USA.

Funding

Pharmacovigilence using Natural Language Processing, Statistics, and the EHRR01LM010016 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI FRIEDMAN, CAROL · 2009 to 2016
$3.4M
NLM NIH HHS R01 LM010016
6 · The paper itself

Abstract

introductionObservational healthcare data contain information useful for hastening detection of adverse drug reactions (ADRs) that may be missed by using data in spontaneous reporting systems (SRSs) alone. There are only several papers describing methods that integrate evidence from healthcare databases and SRSs. We propose a methodology that combines ADR signals from these two sources.

objectivesThe aim of this study was to investigate whether the proposed method would result in more accurate ADR detection than methods using SRSs or healthcare data alone. RESEARCH

designWe applied the method to four clinically serious ADRs, and evaluated it using three experiments that involve combining an SRS with a single facility small-scale electronic health record (EHR), a larger scale network-based EHR, and a much larger scale healthcare claims database. The evaluation used a reference standard comprising 165 positive and 234 negative drug-ADR pairs. MEASURES: Area under the receiver operator characteristics curve (AUC) was computed to measure performance.

resultsThere was no improvement in the AUC when the SRS and small-scale HER were combined. The AUC of the combined SRS and large-scale EHR was 0.82 whereas it was 0.76 for each of the individual systems. Similarly, the AUC of the combined SRS and claims system was 0.82 whereas it was 0.76 and 0.78, respectively, for the individual systems.

conclusionsThe proposed method resulted in a significant improvement in the accuracy of ADR detection when the resources used for combining had sufficient amounts of data, demonstrating that the method could integrate evidence from multiple sources and serve as a tool in actual pharmacovigilance practice.

Indexed as

Adverse Drug Reaction Reporting SystemsDatabases, FactualDelivery of Health CareDrug-Related Side Effects and Adverse ReactionsElectronic Health RecordsFemaleHealth PersonnelHumansMalePharmacovigilance

Identifiers

PMID26153397
PMCPMC4579260

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.