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


Journal

The Lancet. Digital health
ISSN: 2589-7500
Titre abrégé: Lancet Digit Health
Pays: England
ID NLM: 101751302

Informations de publication

Date de publication:
02 2021
Historique:
received: 31 08 2020
revised: 29 10 2020
accepted: 13 11 2020
pubmed: 22 12 2020
medline: 22 12 2020
entrez: 21 12 2020
Statut: ppublish

Résumé

Angiotensin-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. In 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. Among 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. No 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. Wellcome 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.

Sections du résumé

BACKGROUND
Angiotensin-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.
METHODS
In 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.
FINDINGS
Among 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.
INTERPRETATION
No 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.
FUNDING
Wellcome 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.

Identifiants

pubmed: 33342753
pii: S2589-7500(20)30289-2
doi: 10.1016/S2589-7500(20)30289-2
pmc: PMC7834915
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Comment

Langues

eng

Sous-ensembles de citation

IM

Pagination

e98-e114

Subventions

Organisme : Versus Arthritis
ID : 21605
Pays : United Kingdom
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States

Commentaires et corrections

Type : CommentOn
Type : UpdateOf
Type : CommentIn

Informations de copyright

Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Auteurs

Daniel R Morales (DR)

Division of Population Health and Genomics, University of Dundee, Dundee, UK.

Mitchell M Conover (MM)

Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA.

Seng Chan You (SC)

Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.

Nicole Pratt (N)

Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.

Kristin Kostka (K)

Real World Solutions, IQVIA, Cambridge, MA, USA.

Talita Duarte-Salles (T)

Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.

Sergio Fernández-Bertolín (S)

Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.

Maria Aragón (M)

Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.

Scott L DuVall (SL)

Department of Veterans Affairs, Salt Lake City, UT, USA; University of Utah School of Medicine, Salt Lake City, UT, USA.

Kristine Lynch (K)

Department of Veterans Affairs, Salt Lake City, UT, USA; University of Utah School of Medicine, Salt Lake City, UT, USA.

Thomas Falconer (T)

Department of Biomedical Informatics, Columbia University, New York, NY, USA.

Kees van Bochove (K)

The Hyve, Utrecht, Netherlands.

Cynthia Sung (C)

Health Services and Systems Research, Duke-NUS Medical School, Singapore.

Michael E Matheny (ME)

Geriatric 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 Lambert (CG)

Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.

Fredrik Nyberg (F)

School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Thamir M Alshammari (TM)

Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia.

Andrew E Williams (AE)

Tufts Medical Center, Tufts University, Boston, MA, USA.

Rae Woong Park (RW)

Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.

James Weaver (J)

Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA.

Anthony G Sena (AG)

Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.

Martijn J Schuemie (MJ)

Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA.

Peter R Rijnbeek (PR)

Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.

Ross D Williams (RD)

Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.

Jennifer C E Lane (JCE)

Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Albert Prats-Uribe (A)

Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Lin Zhang (L)

School 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 Areia (C)

Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Harlan M Krumholz (HM)

Section of Cardiovascular Medicine, Department of Medicine, Yale University, New Haven, CT, USA.

Daniel Prieto-Alhambra (D)

Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Patrick B Ryan (PB)

Division of Population Health and Genomics, University of Dundee, Dundee, UK; Department of Biomedical Informatics, Columbia University, New York, NY, USA.

George Hripcsak (G)

Department of Biomedical Informatics, Columbia University, New York, NY, USA.

Marc A Suchard (MA)

Department 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.

Classifications MeSH