Evidence map›Paper›PMID 33342753›Full record

ArticleThe Lancet. Digital health2021

Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis.

Daniel R Morales, Mitchell M Conover, Seng Chan You, Nicole Pratt, Kristin Kostka, Talita Duarte-Salles, Sergio Fernández-Bertolín, Maria Aragón, Scott L DuVall, Kristine Lynch and 23 more

Open access · goldAbstract readComment
In one paragraph

Article in The Lancet. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed, 6 pooled it
2.9field-weighted citation impact, top 5% 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

83 citing papers in PubMed, 6 syntheses or guidelines pooled it, 128 citations in OpenAlex.

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  20. Modeling normal bladder injury after radiation therapy.International journal of radiation biology · 2023
    Article

23 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

33 authors at 20 institutions in 10 countries.

Daniel R MoralesDivision of Population Health and Genomics, University of Dundee, Dundee, UK.
Mitchell M ConoverObservational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA.
Seng Chan YouDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.
Nicole PrattQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.
Kristin KostkaReal World Solutions, IQVIA, Cambridge, MA, USA.
Talita Duarte-SallesFundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Sergio Fernández-BertolínFundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Maria AragónFundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Scott L DuVallDepartment of Veterans Affairs, Salt Lake City, UT, USA; University of Utah School of Medicine, Salt Lake City, UT, USA.
Kristine LynchDepartment of Veterans Affairs, Salt Lake City, UT, USA; University of Utah School of Medicine, Salt Lake City, UT, USA.
Thomas FalconerDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Kees van BochoveThe Hyve, Utrecht, Netherlands.
Cynthia SungHealth Services and Systems Research, Duke-NUS Medical School, Singapore.
Michael E MathenyGeriatric Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Christophe G LambertDepartment of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Fredrik NybergSchool of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Thamir M AlshammariMedication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia.
Andrew E WilliamsTufts Medical Center, Tufts University, Boston, MA, USA.
Rae Woong ParkDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.
James WeaverObservational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA.
Anthony G SenaObservational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Martijn J SchuemieObservational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA.
Peter R RijnbeekDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Ross D WilliamsDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Jennifer C E LaneCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
Albert Prats-UribeCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
Lin ZhangSchool of Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China; Melbourne School of Public Health, The University of Melbourne, VIC, Australia.
Carlos AreiaNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Harlan M KrumholzSection of Cardiovascular Medicine, Department of Medicine, Yale University, New Haven, CT, USA.
Daniel Prieto-AlhambraCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
Patrick B RyanDivision of Population Health and Genomics, University of Dundee, Dundee, UK; Department of Biomedical Informatics, Columbia University, New York, NY, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Marc A SuchardDepartment of Biostatistics, Fielding School of Public Health, and Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: msuchard@ucla.edu.
Janssen (United States) · USColumbia University · USInstitut Universitari d'Investigació en Atenció Primària Jordi Gol · ESUniversity of Oxford · GBAjou University · KRErasmus MC · NLUniversity of Utah · USDuke-NUS Medical School · SGIQVIA (United States) · USKing Saud University · SANuffield Orthopaedic Centre · GBThe Hyve (Netherlands) · NLTufts University · USUniversity of California, Los Angeles · USUniversity of Dundee · GBUniversity of Gothenburg · SEUniversity of Melbourne · AUUniversity of New Mexico · USUniversity of South Australia · AUVanderbilt University Medical Center · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863Versus Arthritis 21605
6 · The paper itself

Abstract

backgroundAngiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension.

methodsIn this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296.

findingsAmong 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons.

interpretationNo clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19.

fundingWellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network.

Identifiers

PMID33342753
PMCPMC7834915
OpenAlexW3110951880

What OpenQuestion holds

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LicenceCC BY
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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.