Evidence map›Paper›PMID 31391106›Full record

ArticleBMC medical informatics and decision making2019

Comparing drug safety of hepatitis C therapies using post-market data.

Jing Huang, Xinyuan Zhang, Jiayi Tong, Jingcheng Du, Rui Duan, Liu Yang, Jason H Moore, Cui Tao, Yong Chen

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
  4. 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

9 authors at 3 institutions in 1 country.

Jing HuangDepartmant of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. jing14@upenn.edu.
Xinyuan ZhangSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Jiayi TongDepartmant of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jingcheng DuSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Rui DuanDepartmant of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Liu YangDivision of Transplant Medicine, Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA.
Jason H MooreDepartmant of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Cui TaoSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. cui.tao@uth.tmc.edu.
Yong ChenDepartmant of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. ychen123@upenn.edu.
University of Pennsylvania · USThe University of Texas Health Science Center at Houston · USMayo Clinic in Florida · US

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Biomedical Computing and Informatics Strategies for Infectious Disease ResearchR01AI116794 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2016 to 2020
$2.9M
Patient Medical History Representation, Extraction, and Inference from EHR DataR01LM011829 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2014 to 2018
$1.7M
A General Framework to Account for Outcome Reporting Bias in Systematic ReviewsR01LM012607 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG · 2017 to 2020
$1.4M
NIAID NIH HHS R01 AI116794NIAID NIH HHS R01 AI130460NLM NIH HHS R01 LM011829NLM NIH HHS R01 LM012607
6 · The paper itself

Abstract

backgroundHepatitis C affects about 3 % of the world's population. In the United States, about 3.5 million have chronic hepatitis C, and it is the leading cause of liver cancer and the most common indication for liver transplantation. In the last decades, new advances in therapy have substantially increased the cure rate of hepatitis C to more than 95% with the use of antiviral agents. However, drug safety of the new treatments remains one of the major concerns. Data from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and the Electronic Health Record (EHR) systems provide crucial post-market information to evaluate drug safety. Currently, quantitative evidence of drug safety of hepatitis C treatments based on post-market data are still limited, and there is also a lack of a standard statistical procedure to systematically compare drug safety across multiple drugs using FAERS and EHR.

methodIn this study, we presented a statistical procedure to compare the difference in adverse events (AE) across multiple hepatitis C drugs using data from FAERS and EHR, and to assess the consistency of results from two data bases. Through three major steps, including descriptive comparison, testing for difference among groups, and quantification of association, the proposed method can provide a quantitative comparison on safety of multiple drugs. Specifically, we compared drugs that were approved by FDA to treat hepatitis C before 2011versus those approved after 2013. We used spontaneous AE reports submitted between 2004 to 2015 from FAERS data base and medical records between 1999 to 2015 from the Cerner health facts data base to estimate and compare the rate of AE after drug use.

resultWe studied 30 most frequently reported AEs after treatment of hepatitis C, comparing the difference between drugs approved before 2011versus those approved after 2013. Our results showed that there was difference in rate of AE between the two groups of treatment. We reported the AEs that have significant statistical difference, and estimate the difference attributable to variation of age and gender between the two groups of drug users. Our findings are consistent with results in existing literature. Moreover, we compared the results obtained from FAERS data and EHR data, and evaluated the consistency of evidence.

conclusionThe proposed procedure is a general and standardized pipeline that can be used to compare and visualize drug safety among multiple drugs to support regulatory decision-makings using post-market data. We showed that there was statistically significant difference in AE rates between the new and old therapies for hepatitis C. We showed that both FAERS and EHR contained large information for research of post-market drug safety, but each has its own strength and limitations. Cautions should be taken when combining evidence from the two data resources and there is a need of more sophisticated informatics and statistical tools for evidence synthesis.

Indexed as

Adverse Drug Reaction Reporting SystemsAntiviral AgentsDatabases, FactualDrug-Related Side Effects and Adverse ReactionsHepatitis CHumansProduct Surveillance, PostmarketingUnited StatesUnited States Food and Drug AdministrationAntiviral AgentsAdverse drug reaction reporting systemsData miningElectronic medical recordHepatitis CRegulatory decision support

Identifiers

PMID31391106
PMCPMC6686214
OpenAlexW2965066171

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.