. 2020 Oct 6;11(1):5009.

doi: 10.1038/s41467-020-18849-z.

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

Edward Burn^#^{1

2}, Seng Chan You^#³, Anthony G Sena^{4

5}, Kristin Kostka⁶, Hamed Abedtash⁷, Maria Tereza F Abrahão⁸, Amanda Alberga⁹, Heba Alghoul¹⁰, Osaid Alser¹¹, Thamir M Alshammari¹², Maria Aragon¹, Carlos Areia¹³, Juan M Banda¹⁴, Jaehyeong Cho³, Aedin C Culhane¹⁵, Alexander Davydov^{16

17}, Frank J DeFalco⁴, Talita Duarte-Salles¹, Scott DuVall^{18

19}, Thomas Falconer²⁰, Sergio Fernandez-Bertolin¹, Weihua Gao²¹, Asieh Golozar^{22

23}, Jill Hardin⁴, George Hripcsak^{20

24}, Vojtech Huser²⁵, Hokyun Jeon²⁶, Yonghua Jing²¹, Chi Young Jung²⁷, Benjamin Skov Kaas-Hansen^{28

29}, Denys Kaduk^{16

30}, Seamus Kent³¹, Yeesuk Kim³², Spyros Kolovos³³, Jennifer C E Lane³³, Hyejin Lee³⁴, Kristine E Lynch^{18

19}, Rupa Makadia⁴, Michael E Matheny^{35

36}, Paras P Mehta³⁷, Daniel R Morales³⁸, Karthik Natarajan^{20

24}, Fredrik Nyberg³⁹, Anna Ostropolets²⁰, Rae Woong Park^{3

26}, Jimyung Park²⁶, Jose D Posada⁴⁰, Albert Prats-Uribe², Gowtham Rao⁴, Christian Reich⁶, Yeunsook Rho³³, Peter Rijnbeek⁵, Lisa M Schilling⁴¹, Martijn Schuemie^{4

42}, Nigam H Shah⁴⁰, Azza Shoaibi⁴, Seokyoung Song⁴³, Matthew Spotnitz²⁰, Marc A Suchard⁴², Joel N Swerdel⁴, David Vizcaya⁴⁴, Salvatore Volpe²⁰, Haini Wen⁴⁵, Andrew E Williams⁴⁶, Belay B Yimer⁴⁷, Lin Zhang^{48

49}, Oleg Zhuk¹⁶, Daniel Prieto-Alhambra⁵⁰, Patrick Ryan^{4

51}

Affiliations

¹ Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
² Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK.
³ Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
⁴ Janssen Research and Development, Titusville, NJ, USA.
⁵ Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
⁶ Real World Solutions, IQVIA, Cambridge, MA, USA.
⁷ Eli Lilly and Company, Indianapolis, IN, USA.
⁸ Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil.
⁹ Observational Health Data Sciences and Informatics Network, Alberta, Canada.
¹⁰ Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine.
¹¹ Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
¹² Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia.
¹³ Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
¹⁴ Department of Computer Science, Georgia State University, Atlanta, GA, USA.
¹⁵ Data Science, Dana-Farber Cancer Institute. Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
¹⁶ Odysseus Data Services, Inc., Cambridge, MA, USA.
¹⁷ Department for Microbiology, Virology and Immunology, Belarusian State Medical University, Minsk, Belarus.
¹⁸ Department of Veterans Affairs, Salt Lake City, UT, USA.
¹⁹ University of Utah School of Medicine, Salt Lake City, UT, USA.
²⁰ Department of Biomedical Informatics, Columbia University, New York, NY, USA.
²¹ Health Economics and Outcomes Research, AbbVie, North Chicago, IL, USA.
²² Pharmacoepidemiology, Regeneron, NY, USA.
²³ Department of Epidemiology, Johns Hopkins School of Public, Baltimore, MD, USA.
²⁴ New York-Presbyterian Hospital, New York, NY, USA.
²⁵ National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
²⁶ Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
²⁷ Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, Korea.
²⁸ Clinical Pharmacology Unit, Zealand University Hospital, Køge, Denmark.
²⁹ NNF Centre for Protein Research, University of Copenhagen, København, Denmark.
³⁰ Department of Pediatrics № 2, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine.
³¹ Science Policy and Research, National Institute for Health and Care Excellence, London, UK.
³² Department of Orthopaedic Surgery, College of Medicine, Hanyang University, Seoul, Korea.
³³ Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK.
³⁴ Bigdata Department, Health Insurance Review & Assessment Service, Wonju, Korea.
³⁵ GRECC, Tennessee Valley Healthcare System VA, Nashville, TN, USA.
³⁶ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
³⁷ College of Medicine-Tucson, University of Arizona, Tucson, AZ, USA.
³⁸ Division of Population Health and Genomics, University of Dundee, Dundee, UK.
³⁹ School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁴⁰ Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
⁴¹ Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁴² Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA.
⁴³ Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Gyeongsan, Korea.
⁴⁴ Bayer Pharmaceuticals, Barcelona, Spain.
⁴⁵ Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁴⁶ Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA.
⁴⁷ Centre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
⁴⁸ School of Public Health, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
⁴⁹ Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
⁵⁰ Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK. daniel.prietoalhambra@ndorms.ox.ac.uk.
⁵¹ Columbia University, New York, NY, USA.

^# Contributed equally.

PMID: 33024121
PMCID: PMC7538555
DOI: 10.1038/s41467-020-18849-z

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

Edward Burn et al. Nat Commun. 2020.

. 2020 Oct 6;11(1):5009.

doi: 10.1038/s41467-020-18849-z.

Authors

Affiliations

¹ Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
² Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK.
³ Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
⁴ Janssen Research and Development, Titusville, NJ, USA.
⁵ Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
⁶ Real World Solutions, IQVIA, Cambridge, MA, USA.
⁷ Eli Lilly and Company, Indianapolis, IN, USA.
⁸ Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil.
⁹ Observational Health Data Sciences and Informatics Network, Alberta, Canada.
¹⁰ Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine.
¹¹ Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
¹² Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia.
¹³ Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
¹⁴ Department of Computer Science, Georgia State University, Atlanta, GA, USA.
¹⁵ Data Science, Dana-Farber Cancer Institute. Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
¹⁶ Odysseus Data Services, Inc., Cambridge, MA, USA.
¹⁷ Department for Microbiology, Virology and Immunology, Belarusian State Medical University, Minsk, Belarus.
¹⁸ Department of Veterans Affairs, Salt Lake City, UT, USA.
¹⁹ University of Utah School of Medicine, Salt Lake City, UT, USA.
²⁰ Department of Biomedical Informatics, Columbia University, New York, NY, USA.
²¹ Health Economics and Outcomes Research, AbbVie, North Chicago, IL, USA.
²² Pharmacoepidemiology, Regeneron, NY, USA.
²³ Department of Epidemiology, Johns Hopkins School of Public, Baltimore, MD, USA.
²⁴ New York-Presbyterian Hospital, New York, NY, USA.
²⁵ National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
²⁶ Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
²⁷ Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, Korea.
²⁸ Clinical Pharmacology Unit, Zealand University Hospital, Køge, Denmark.
²⁹ NNF Centre for Protein Research, University of Copenhagen, København, Denmark.
³⁰ Department of Pediatrics № 2, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine.
³¹ Science Policy and Research, National Institute for Health and Care Excellence, London, UK.
³² Department of Orthopaedic Surgery, College of Medicine, Hanyang University, Seoul, Korea.
³³ Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK.
³⁴ Bigdata Department, Health Insurance Review & Assessment Service, Wonju, Korea.
³⁵ GRECC, Tennessee Valley Healthcare System VA, Nashville, TN, USA.
³⁶ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
³⁷ College of Medicine-Tucson, University of Arizona, Tucson, AZ, USA.
³⁸ Division of Population Health and Genomics, University of Dundee, Dundee, UK.
³⁹ School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁴⁰ Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
⁴¹ Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁴² Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA.
⁴³ Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Gyeongsan, Korea.
⁴⁴ Bayer Pharmaceuticals, Barcelona, Spain.
⁴⁵ Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁴⁶ Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA.
⁴⁷ Centre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
⁴⁸ School of Public Health, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
⁴⁹ Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
⁵⁰ Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK. daniel.prietoalhambra@ndorms.ox.ac.uk.
⁵¹ Columbia University, New York, NY, USA.

^# Contributed equally.

PMID: 33024121
PMCID: PMC7538555
DOI: 10.1038/s41467-020-18849-z

Abstract

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

PubMed Disclaimer

Conflict of interest statement

All authors have completed the ICMJE uniform disclosure form, with the following declarations made: D.P.A. reports grants and other from AMGEN, grants, non-financial support and other from UCB Biopharma, grants from Les Laboratoires Servier, outside the submitted work; and Janssen, on behalf of IMI-funded EHDEN and EMIF consortiums, and Synapse Management Partners have supported training programmes organised by DPA’s department and open for external participants. D.V. reports personal fees from Bayer, outside the submitted work, and he is a full-time employee at a pharmaceutical company. DM reports funding support from the Wellcome Trust, NIHR, Scottish CSO and Tenovus Scotland for research unrelated to this work. S.C.Y. reports grants from Korean Ministry of Health & Welfare, grants from Korean Ministry of Trade, Industry & Energy, during the conduct of the study. A.G. reports personal fees from Regeneron Pharmaceuticals, outside the submitted work, and she a full-time employee at Regeneron Pharmaceuticals. This work was not conducted at Regeneron Pharmaceuticals. Y.J. reports employee of AbbVie and owns company stock. A.A. reports that he is currently employed at Alberta Health Services (AHS) as a Data Science Lead redeployed as an epidemiologist to aid in the COVID-19 response. This work was not conducted at AHS, within AHS working hours, or with AHS staff. He contributed and conducted this work as an Independent Epidemiologist, as a member of the Observational Health Data Sciences and Informatics (OHDSI) Network. P.R. reports grants from Innovative Medicines Initiative, grants from Janssen Research and Development, during the conduct of the study. M.S. reports grants from US National Science Foundation, grants from US National Institutes of Health, grants from IQVIA, personal fees from Janssen Research and Development, during the conduct of the study. G.H. reports grants from US NIH National Library of Medicine, during the conduct of the study; grants from Janssen Research, outside the submitted work. A.P.U. reports grants from Fundacion Alfonso Martin Escudero, grants from Medical Research Council, outside the submitted work. H.A. reports personal fees from Eli Lilly and Company, outside the submitted work. A.S. reports personal fees from Janssen Research & Development, during the conduct of the study; personal fees from Janssen Research & Development, outside the submitted work. A.S. is a full-time employee of Janssen and shareholder of Johnson & Johnson. G.R. is a full-time employee of Janssen and shareholder of Johnson & Johnson. F.D. reports personal fees from Janssen Research & Development, during the conduct of the study; personal fees from Janssen Research & Development, outside the submitted work. R.W.P. reports grants from Korean Ministry of Health & Welfare, grants from Korean Ministry of Trade, Industry & Energy, during the conduct of the study. J.P. reports grants from Korean Ministry of Health & Welfare, grants from Korean Ministry of Trade, Industry & Energy, during the conduct of the study. J.C. reports grants from Korean Ministry of Health & Welfare, grants from Korean Ministry of Trade, Industry & Energy, during the conduct of the study. S.D. reports grants from Anolinx, LLC, grants from Astellas Pharma, Inc, grants from AstraZeneca Pharmaceuticals LP, grants from Boehringer Ingelheim International GmbH, grants from Celgene Corporation, grants from Eli Lilly and Company, grants from Genentech Inc., grants from Genomic Health, Inc., grants from Gilead Sciences Inc., grants from GlaxoSmithKline PLC, grants from Innocrin Pharmaceuticals Inc., grants from Janssen Pharmaceuticals, Inc., grants from Kantar Health, grants from Myriad Genetic Laboratories, Inc., grants from Novartis International AG, grants from Parexel International Corporation through the University of Utah or Western Institute for Biomedical Research outside the submitted work. H.J. reports grants from Korean Ministry of Health & Welfare, grants from Korean Ministry of Trade, Industry & Energy, during the conduct of the study. B.S.K.H. reports grants from Innovation Fund Denmark (5153-00002B) and the Novo Nordisk Foundation (NNF14CC0001), outside the submitted work. K.K. reports she is an employee of IQVIA. CR reports he is an employee of IQVIA. J.S. reports other from Janssen R&D, during the conduct of the study; other from Janssen R&D, outside the submitted work; and J.S. was a full-time employee of Johnson & Johnson, or a subsidiary, at the time the study was conducted. J.S. owns stock, stock options, and pension rights from the company. R.M. reports and is an employee of Janssen Research and Development. W.G. is an AbbVie employee. P.R. reports and is an employee of Janssen Research and Development and shareholder of Johnson & Johnson. M.S. is a full-time employee of Janssen R&D, and a shareholder of Johnson & Johnson. J.H. reports other from Janssen Research & Development, during the conduct of the study; other from Janssen Research & Development, outside the submitted work; and full-time employee of Janssen and shareholder of Johnson & Johnson. All other authors declare no competing interests.

Figures

**Fig. 1. Age of patients hospitalised with COVID-19 and of patients hospitalised with influenza.**
Individuals hospitalised with COVID-19 between December 2019 and April 2020 compared with those hospitalised with influenza between September 2014 to April 2019 (where available). Proportion of cohorts by 5-year age groups, with groups with counts of <10 omitted. CUIMC: Columbia University Irving Medical Center; HIRA: Health Insurance Review & Assessment; HM: HM Hospitales; PHD: Premier Healthcare Database; SIDIAP: The Information System for Research in Primary Care; UC HDC: University of Colorado Health Data Compass; VA OMOP: Department of Veterans Affairs. Influenza data for SIDIAP was only available from 2014 to 2017.

**Fig. 2. Prevalence of conditions and medication use among COVID-19 patients.**
Individuals hospitalised with COVID-19 between December 2019 and April 2020. Conditions from up to a year prior, medication use from day of hospitalisation. Each dot represents one of these covariates with the colour indicating the type of condition/medication. CUIMC: Columbia University Irving Medical Center; HIRA: Health Insurance Review & Assessment; HM: HM Hospitales; PHD: Premier Healthcare Database; SIDIAP: The Information System for Research in Primary Care; UC HDC: University of Colorado Health Data Compass; VA OMOP: Department of Veterans Affairs.

**Fig. 3. Standardised mean difference in conditions (top) and medication use (bottom) among COVID-19 patients compared to 2014–2019 influenza patients.**
Individuals hospitalised with COVID-19 between December 2019 and April 2020 compared with those hospitalised with influenza between September 2014 and April 2019. Conditions from up to a year prior, medication use from day of hospitalisation. Each dot represents one of these covariates with the colour indicating the type of condition/medication and the size of the dot reflecting the prevalence of the variable in the COVID-19 study populations. CUIMC: Columbia University Irving Medical Center; PHD: Premier Healthcare Database; SIDIAP: The Information System for Research in Primary Care; VA OMOP: Department of Veterans Affairs.

**Fig. 4. Characteristics of COVID-19 patients compared to 2014–2019 Influenza patients.**
The plot compares demographics (age and sex), conditions (recorded over the year prior and up to the day of hospitalisation), and medications (1) from a year prior up to the day of hospitalisation, (2) from 30 days prior up to the day of hospitalisation and (3) on day of hospitalisation). Each dot represents one of these covariates with the colour indicating the absolute value of the standardised mean difference (SMD), with a SMD above 0.1 taken to indicate a difference in the prevalence of a particular covariate. The proportion male, with heart disease, with chronic obstructive pulmonary disease (COPD), and taking immunosuppressants (over the 30 days prior up to hospitalisation) are shown for illustration. CUIMC: Columbia University Irving Medical Center; PHD: Premier Healthcare Database; SIDIAP: The Information System for Research in Primary Care; UC HDC: University of Colorado Health Data Compass; VA OMOP: Department of Veterans Affairs.

See this image and copyright information in PMC

Update of

Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study.
Burn E, You SC, Sena A, Kostka K, Abedtash H, Abrahao MTF, Alberga A, Alghoul H, Alser O, Alshammari TM, Aragon M, Areia C, Banda JM, Cho J, Culhane AC, Davydov A, DeFalco FJ, Duarte-Salles T, DuVall SL, Falconer T, Fernandez-Bertolin S, Gao W, Golozar A, Hardin J, Hripcsak G, Huser V, Jeon H, Jing Y, Jung CY, Kaas-Hansen BS, Kaduk D, Kent S, Kim Y, Kolovos S, Lane J, Lee H, Lynch KE, Makadia R, Matheny ME, Mehta P, Morales DR, Natarajan K, Nyberg F, Ostropolets A, Park RW, Park J, Posada JD, Prats-Uribe A, Rao GA, Reich C, Rho Y, Rijnbeek P, Schilling LM, Schuemie M, Shah NH, Shoaibi A, Song S, Spotnitz M, Suchard MA, Swerdel J, Vizcaya D, Volpe S, Wen H, Williams AE, Yimer BB, Zhang L, Zhuk O, Prieto-Alhambra D, Ryan P. Burn E, et al. medRxiv [Preprint]. 2020 Jun 28:2020.04.22.20074336. doi: 10.1101/2020.04.22.20074336. medRxiv. 2020. Update in: Nat Commun. 2020 Oct 6;11(1):5009. doi: 10.1038/s41467-020-18849-z. PMID: 32511443 Free PMC article. Updated. Preprint.

References

1. Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed
1. Wang D, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA. 2020;323:1061–1069. doi: 10.1001/jama.2020.1585. - DOI - PMC - PubMed
1. Bhatraju, P. K. et al. Covid-19 in critically ill patients in the seattle region—case series. N. Engl. J. Med. 10.1056/NEJMoa2004500 (2020). - PMC - PubMed
1. Verity R, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet. 2020;3099:1–9. - PMC - PubMed
1. Mertz D, et al. Populations at risk for severe or complicated influenza illness: systematic review and meta-analysis. BMJ Br. Med. J. 2013;347:f5061. doi: 10.1136/bmj.f5061. - DOI - PMC - PubMed

Publication types

Actions
Actions
Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
Medical
- MedlinePlus Health Information

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

Affiliations

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical