Evidence map›Paper›PMID 34812146›Full record

ArticleeLife2021

Data mining methodology for response to hypertension symptomology-application to COVID-19-related pharmacovigilance.

Xuan Xu, Jessica Kawakami, Nuwan Indika Millagaha Gedara, Jim E Riviere, Emma Meyer, Gerald J Wyckoff, Majid Jaberi-Douraki

Open access · goldAbstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
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  4. Predicting the Prognostic Value ofInternational journal of molecular sciences · 2022
    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

7 authors at 3 institutions in 2 countries.

Xuan Xu1DATA Consortium, www.1DATA.life, Olathe, United States.ORCID 0000-0003-3349-7183
Jessica Kawakami1DATA Consortium, www.1DATA.life, Olathe, United States.ORCID 0000-0002-7141-5698
Nuwan Indika Millagaha Gedara1DATA Consortium, www.1DATA.life, Olathe, United States.
Jim E Riviere1DATA Consortium, www.1DATA.life, Olathe, United States.
Emma Meyer1DATA Consortium, www.1DATA.life, Olathe, United States.
Gerald J Wyckoff1DATA Consortium, www.1DATA.life, Olathe, United States.
Majid Jaberi-Douraki1DATA Consortium, www.1DATA.life, Olathe, United States.ORCID 0000-0002-8505-6550
Kansas State University · USUniversity of Missouri–Kansas City · USNorth Carolina State University · US

Funding

BioNexus KC 20-7
6 · The paper itself

Abstract

Background: Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Methods: Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO. Results: Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy. Conclusions: We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness. Funding: GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.

Indexed as

Drug-Related Side Effects and Adverse ReactionsPharmacovigilanceAdverse Drug Reaction Reporting SystemsAngiotensin-Converting Enzyme InhibitorsAntihypertensive AgentsBayes TheoremCalcium Channel BlockersCOVID-19Data MiningFibrinolytic AgentsHumansHypertensionSARS-CoV-2Angiotensin-Converting Enzyme InhibitorsAntihypertensive AgentsCalcium Channel BlockersFibrinolytic Agentsartificial intelligencecomputational biologyCOVID-19-related pharmacovigilancedata-driven methodologyFDA ADE databasehypertensionmedicinenonepulmonary symptomologysystems biology

Identifiers

PMID34812146
PMCPMC8754433
OpenAlexW3217789531

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.