Evidence map›Paper›PMID 40065662›Full record

ArticleJournal of clinical hypertension (Greenwich, Conn.)2025

Drug-Related Hypertension: A Disproportionality Analysis Leveraging the FDA Adverse Event Reporting System.

Hao Zhu, Linwei Pan, Hannah Lui, Jing Zhang

Abstract read
In one paragraph

Article in Journal of clinical hypertension (Greenwich, Conn.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Hao ZhuDepartment of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.ORCID 0009-0002-7317-0863
Linwei PanGraduate School, Tsinghua University, Beijing, China.
Hannah LuiDepartment of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Jing ZhangThe Second Department of Infectious Disease, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertension exerts a significant global disease burden, adversely affecting the well-being of billions. Alarmingly, drug-related hypertension remains an area that has not been comprehensively investigated. Therefore, this study is designed to utilize the adverse event reports (AERs) from the US Food and Drug Administration's Adverse Event Reporting System (FAERS) to more comprehensively identify drugs that may potentially lead to hypertension. Specifically, a total of 207 233 AERs were extracted from FAERS, spanning the time period from 2004 to 2024. Based on these reports, this study presented the top 40 drugs most frequently reported to be associated with post-administration hypertension in different genders. Furthermore, we employed four disproportionality analysis methods, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayes Geometric Mean (EBGM), to pinpoint the top three drugs with strongest signals in relation to hypertension across different age and gender subgroups. Some drugs, such as rofecoxib, lenvatinib, and celecoxib, were found to appear on both the frequency and signal strength lists. These results contribute to a more comprehensive understanding of the cardiovascular safety profiles of pharmacological agents, suggesting the necessity of blood pressure monitoring following administration.

Indexed as

Adverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsHypertensionAdultAgedAntihypertensive AgentsBayes TheoremFemaleHumansMaleMiddle AgedUnited StatesUnited States Food and Drug AdministrationAntihypertensive Agentsadverse drug eventsdisproportionality analysisdrug‐related hypertensionFAERS

Identifiers

PMID40065662
PMCPMC11894037

What OpenQuestion holds

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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.