Evidence mapPaperPMID 37995049Full record

ArticleDrug safety2024

Patient-Reported Reasons for Antihypertensive Medication Change: A Quantitative Study Using Social Media.

Cristina Micale, Su Golder, Karen O'Connor, Davy Weissenbacher, Robert Gross, Sean Hennessy, Graciela Gonzalez-Hernandez

Erratum issuedAbstract read
PubMed Publisher
In one paragraph

Article in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Cristina MicalePerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. cvm006@students.jefferson.edu.ORCID 0009-0000-5429-6095
Su GolderDepartment of Health Sciences, University of York, York, UK.
Karen O'ConnorDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Davy WeissenbacherDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA.
Robert GrossDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Sean HennessyDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Graciela Gonzalez-HernandezDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA.
University of Pennsylvania · USCedars-Sinai Medical Center · USThomas Jefferson University · USUniversity of York · GB

Funding

Social Media Mining for PharmacovigilanceR01LM011176 · NLM · UNIVERSITY OF PENNSYLVANIA · PI GONZALEZ HERNANDEZ, GRACIELA, SHEN, LI · 2012 to 2021
$4.5M
NLM NIH HHS R01 LM011176U.S. National Library of Medicine NIH NLM 1R01U.S. National Library of Medicine R01LM011176
6 · The paper itself

Abstract

introductionHypertension is the leading cause of heart disease in the world, and discontinuation or nonadherence of antihypertensive medication constitutes a significant global health concern. Patients with hypertension have high rates of medication nonadherence. Studies of reasons for nonadherence using traditional surveys are limited, can be expensive, and suffer from response, white-coat, and recall biases. Mining relevant posts by patients on social media is inexpensive and less impacted by the pressures and biases of formal surveys, which may provide direct insights into factors that lead to non-compliance with antihypertensive medication.

methodsThis study examined medication ratings posted to WebMD, an online health forum that allows patients to post medication reviews. We used a previously developed natural language processing classifier to extract indications and reasons for changes in angiotensin receptor II blocker (ARB) and angiotensin-converting enzyme inhibitor (ACEI) treatments. After extraction, ratings were manually annotated and compared with data from the US Food and Drug administration (FDA) Adverse Events Reporting System (FAERS) public database.

resultsFrom a collection of 343,459 WebMD reviews, we automatically extracted 1867 posts mentioning changes in ACEIs or ARBs, and manually reviewed the 300 most recent posts regarding ACEI treatments and the 300 most recent posts regarding ARB treatments. After excluding posts that only mentioned a dose change or were a false-positive mention, 142 posts in the ARBs dataset and 187 posts in the ACEIs dataset remained. The majority of posts (97% ARBs, 91% ACEIs) indicated experiencing an adverse event as the reason for medication change. The most common adverse events reported mapped to the Medical Dictionary for Regulatory Activities were "musculoskeletal and connective tissue disorders" like muscle and joint pain for ARBs, and "respiratory, thoracic, and mediastinal disorders" like cough and shortness of breath for ACEIs. These categories also had the largest differences in percentage points, appearing more frequently on WebMD data than FDA data (p < 0.001).

conclusionMusculoskeletal and respiratory symptoms were the most commonly reported adverse effects in social media postings associated with drug discontinuation. Managing such symptoms is a potential target of interventions seeking to improve medication persistence.

Indexed as

HypertensionSocial MediaAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsAntihypertensive AgentsHumansPatient Reported Outcome MeasuresAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsAntihypertensive Agents

Identifiers

PMID37995049
OpenAlexW4388946493

What Socratic holds

Textmetadata
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 Socratic graph.