Evidence map›Paper›PMID 35854736›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2022

A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs.

Zhizhen Zhao, Ruoqi Liu, Lei Wang, Lang Li, Chi Song, Ping Zhang

Open access · greenAbstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

6 authors at 1 institution in 1 country.

Zhizhen ZhaoDepartment of Statistics, The Ohio State University, Columbus, Ohio, USA.
Ruoqi LiuComputer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
Lei WangDepartment of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.
Lang LiDepartment of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.
Chi SongDivision of Biostatistics, The Ohio State University, Columbus, Ohio, USA.
Ping ZhangComputer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
The Ohio State University · US

Funding

A Statistical Network Pharmacology Approach for Early Detection of Adverse Drug EventsR01GM141279 · NIGMS · OHIO STATE UNIVERSITY · PI ZHANG, PENGYUE, ZHANG, PING · 2021 to 2023
$937k
NIGMS NIH HHS R01 GM141279
6 · The paper itself

Abstract

The identification of associations between drugs and adverse drug events (ADEs) is crucial for drug safety surveillance. An increasing number of studies have revealed that children and seniors are susceptible to ADEs at the population level. However, the comprehensive explorations of age risks in drug-ADE pairs are still limited. The FDA Adverse Event Reporting System (FAERS) provides individual case reports, which can be used for quantifying different age risks. In this study, we developed a statistical computational framework to detect age group of patients who are susceptible to some ADEs after taking specific drugs. We adopted different Chi-squared tests and conducted disproportionality analysis to detect drug-ADE pairs with age differences. We analyzed 4,580,113 drug-ADE pairs in FAERS (2004 to 2018Q3) and identified 2,523 pairs with the highest age risk. Furthermore, we conducted a case study on statin-induced ADE in children and youth. The code and results are available at https://github.com/Zhizhen- Zhao/Age-Risk-Identification.

Identifiers

PMID35854736
PMCPMC9285161
OpenAlexW4205323708

What OpenQuestion holds

Textmetadata
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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.