Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

Erica A Voss; Azza Shoaibi; Lana Yin Hui Lai; Clair Blacketer; Thamir Alshammari; Rupa Makadia; Kevin Haynes; Anthony G Sena; Gowtham Rao; Sebastiaan van Sandijk; Clement Fraboulet; Laurent Boyer; Tanguy Le Carrour; Scott Horban; Daniel R Morales; Jordi Martínez Roldán; Juan Manuel Ramírez-Anguita; Miguel A Mayer; Marcel de Wilde; Luis H John; Talita Duarte-Salles; Elena Roel; Andrea Pistillo; Raivo Kolde; Filip Maljković; Spiros Denaxas; Vaclav Papez; Michael G Kahn; Karthik Natarajan; Christian Reich; Alex Secora; Evan P Minty; Nigam H Shah; Jose D Posada; Maria Teresa Garcia Morales; Diego Bosca; Honorio Cadenas Juanino; Antonio Diaz Holgado; Miguel Pedrera Jiménez; Pablo Serrano Balazote; Noelia García Barrio; Selçuk Şen; Ali Yağız Üresin; Baris Erdogan; Luc Belmans; Geert Byttebier; Manu L N G Malbrain; Daniel J Dedman; Zara Cuccu; Rohit Vashisht; Atul J Butte; Ayan Patel; Lisa Dahm; Cora Han; Fan Bu; Faaizah Arshad; Anna Ostropolets; Fredrik Nyberg; George Hripcsak; Marc A Suchard; Dani Prieto-Alhambra; Peter R Rijnbeek; Martijn J Schuemie; Patrick B Ryan

doi:10.1016/j.eclinm.2023.101932

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

EClinicalMedicine. 2023 Apr:58:101932. doi: 10.1016/j.eclinm.2023.101932. Epub 2023 Apr 4.

Authors

Erica A Voss^{1

2

3}, Azza Shoaibi^{1

3}, Lana Yin Hui Lai^{1

4}, Clair Blacketer^{1

2

3}, Thamir Alshammari^{1

5}, Rupa Makadia^{1

3}, Kevin Haynes³, Anthony G Sena^{1

2

3}, Gowtham Rao^{1

3}, Sebastiaan van Sandijk^{1

6}, Clement Fraboulet⁷, Laurent Boyer⁷, Tanguy Le Carrour⁸, Scott Horban⁹, Daniel R Morales^{10

11}, Jordi Martínez Roldán¹², Juan Manuel Ramírez-Anguita^{13

14}, Miguel A Mayer^{13

14}, Marcel de Wilde^{1

2}, Luis H John^{1

2}, Talita Duarte-Salles^{1

15}, Elena Roel¹⁵, Andrea Pistillo¹⁵, Raivo Kolde¹⁶, Filip Maljković¹⁷, Spiros Denaxas^{18

19

20}, Vaclav Papez^{18

19}, Michael G Kahn^{1

21}, Karthik Natarajan^{1

22

23}, Christian Reich^{1

24}, Alex Secora²⁴, Evan P Minty^{1

25}, Nigam H Shah^{1

26}, Jose D Posada^{1

27}, Maria Teresa Garcia Morales²⁸, Diego Bosca²⁹, Honorio Cadenas Juanino³⁰, Antonio Diaz Holgado³⁰, Miguel Pedrera Jiménez³¹, Pablo Serrano Balazote³¹, Noelia García Barrio³¹, Selçuk Şen³², Ali Yağız Üresin³², Baris Erdogan³³, Luc Belmans³⁴, Geert Byttebier³⁴, Manu L N G Malbrain^{34

35}, Daniel J Dedman³⁶, Zara Cuccu³⁶, Rohit Vashisht^{1

37}, Atul J Butte^{1

37

38}, Ayan Patel^{1

37}, Lisa Dahm^{1

38}, Cora Han^{1

38}, Fan Bu³⁹, Faaizah Arshad^{1

39}, Anna Ostropolets^{1

22}, Fredrik Nyberg⁴⁰, George Hripcsak^{1

22

23}, Marc A Suchard^{1

39

41}, Dani Prieto-Alhambra^{1

2

42}, Peter R Rijnbeek^{1

2}, Martijn J Schuemie^{1

3

39}, Patrick B Ryan^{1

3

22}

Affiliations

¹ OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA.
² Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.
³ Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA.
⁴ Department of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK.
⁵ College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Kingdom of Saudi Arabia.
⁶ Odysseus Data Services, Prague, Czechia.
⁷ Department of Medical Information, Assistance Publique - Hopitaux de Marseille, Marseille, France.
⁸ Easter Eggs, Paris, France.
⁹ Health Informatics Centre, University of Dundee, Dundee, UK.
¹⁰ Division of Population Health and Genomics, University of Dundee, Dundee, UK.
¹¹ Department of Public Health, University of Southern Denmark, Odense, Denmark.
¹² Hospital del Mar, Department of Innovation and Digital Transformation, Barcelona, Spain.
¹³ Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.
¹⁴ Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
¹⁵ Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain.
¹⁶ University of Tartu, Tartu, Estonia.
¹⁷ Heliant, Belgrade, Serbia.
¹⁸ University College London, Institute of Health Informatics, London, UK.
¹⁹ Health Data Research UK, London, UK.
²⁰ British Heart Foundation Data Science Centre, London, UK.
²¹ University of Colorado Anschutz Medical Campus, Denver, CO, USA.
²² Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
²³ New York-Presbyterian Hospital, New York, NY, USA.
²⁴ IQVIA, Falls Church, VA, USA.
²⁵ O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Alberta, CA, USA.
²⁶ Stanford School of Medicine, Stanford University, Palo Alto, CA, USA.
²⁷ Department of Systems Engineering, School of Engineering, Universidad del Norte, Barranquilla, Colombia.
²⁸ Instituto de Investigación, Hospital 12 de Octubre, Madrid, Spain.
²⁹ VeraTech for Health, Valencia, Spain.
³⁰ Fundación para la Investigación e Innovación Biosanitaria en Atención Primaria (FIIBAP), Madrid, Spain.
³¹ Hospital Universitario 12 de Octubre, Madrid, Spain.
³² Division of Clinical Pharmacology, Department of Medical Pharmacology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Türkiye.
³³ Clinerion, Basel, Switzerland.
³⁴ Medaman BV, Geel, Flanders, Belgium.
³⁵ 1st Department Anaesthesiology and Intensive Therapy, Professor Critical Care Research, Medical University Lublin, Lublin, Poland.
³⁶ Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, UK.
³⁷ Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
³⁸ Center for Data-driven Insights and Innovation, University of California Health, Oakland, CA, USA.
³⁹ Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
⁴⁰ School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁴¹ VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, UT, USA.
⁴² Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.

Abstract

Background: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population.

Methods: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases.

Findings: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism.

Interpretation: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term.

Funding: None.

Keywords: Adverse events of special interest; COVID-19; OMOP CDM; Observational research.