NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study.

COVID-19 NSAIDs cyclooxygenase inhibitors observational study

Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
22 Dec 2021
Historique:
pubmed: 29 4 2021
medline: 29 4 2021
entrez: 28 4 2021
Statut: epublish

Résumé

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Sections du résumé

BACKGROUND BACKGROUND
Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.
METHODS METHODS
A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis.
RESULTS RESULTS
Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations.
CONCLUSIONS CONCLUSIONS
Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Identifiants

pubmed: 33907758
doi: 10.1101/2021.04.13.21255438
pmc: PMC8077581
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIGMS NIH HHS
ID : U54 GM104938
Pays : United States
Organisme : NCATS NIH HHS
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Commentaires et corrections

Type : UpdateIn

Auteurs

Justin T Reese (JT)

Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

Ben Coleman (B)

The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

Lauren Chan (L)

Translational and Integrative Sciences Center, Oregon State University, Corvallis, OR, USA.

Hannah Blau (H)

The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

Tiffany J Callahan (TJ)

Computational Bioscience, University of Colorado Anschutz Medical Campus, Boulder, CO, USA.
Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Luca Cappelletti (L)

AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy.

Tommaso Fontana (T)

AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy.

Katie Rebecca Bradwell (KR)

Palantir Technologies, Denver, CO, USA.

Nomi L Harris (NL)

Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

Elena Casiraghi (E)

AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy.
CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Roma, Italy.

Giorgio Valentini (G)

AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy.
CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Roma, Italy.

Guy Karlebach (G)

The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

Rachel Deer (R)

University of Texas Medical Branch, Galveston, TX, USA.

Julie A McMurry (JA)

Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Melissa A Haendel (MA)

Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Christopher G Chute (CG)

Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA.

Emily Pfaff (E)

North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Richard Moffitt (R)

Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.

Heidi Spratt (H)

University of Texas Medical Branch, Galveston, TX, USA.

Jasvinder Singh (J)

University of Alabama at Birmingham, Birmingham, AL, USA.
Medicine Service, VA Medical Center, Birmingham, AL, USA.

Christopher J Mungall (CJ)

Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

Andrew E Williams (AE)

Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA.
Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies.
Northeastern University, OHDSI Center at the Roux Institute.

Peter N Robinson (PN)

The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA.

Classifications MeSH