Evidence map›Paper›PMID 34362806›Full record

ArticleBMJ open2021

Characteristics and outcomes of hospitalised adults with COVID-19 in a Global Health Research Network: a cohort study.

Julia Zhu, Zhongyuan Wei, Manasi Suryavanshi, Xiu Chen, Qian Xia, Jenny Jiang, Olulade Ayodele, Brian D Bradbury, Corinne Brooks, Carolyn A Brown and 16 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

9 citing papers in PubMed, 10 citations in OpenAlex.

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  7. Measuring SARS-CoV-2 aerosolization in rooms of hospitalized patients.Laryngoscope investigative otolaryngology · 2022
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  9. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors at 4 institutions in 1 country.

Julia ZhuCenter for Observational Research and Data Science, Bristol-Myers Squibb, Princeton, New Jersey, USA.ORCID 0000-0001-8210-8389
Zhongyuan WeiCenter for Observational Research and Data Science, Bristol-Myers Squibb, Princeton, New Jersey, USA.
Manasi SuryavanshiCenter for Observational Research and Data Science, Bristol-Myers Squibb, Princeton, New Jersey, USA.
Xiu ChenCenter for Observational Research and Data Science, Bristol-Myers Squibb, Princeton, New Jersey, USA.
Qian XiaCenter for Observational Research and Data Science, Bristol-Myers Squibb, Princeton, New Jersey, USA.
Jenny JiangCenter for Observational Research and Data Science, Bristol-Myers Squibb, Princeton, New Jersey, USA.
Olulade AyodeleData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Brian D BradburyCenter for Observational Research, Amgen, Inc, Thousand Oaks, California, USA.
Corinne BrooksCenter for Observational Research, Amgen, Inc, Thousand Oaks, California, USA.
Carolyn A BrownCenter for Observational Research, Amgen, Inc, Thousand Oaks, California, USA.
Alvan ChengCenter for Observational Research, Amgen, Inc, Thousand Oaks, California, USA.
Cathy W CritchlowResearch & Development Strategy & Operations, Amgen, Inc, Thousand Oaks, California, USA.
Giovanna DevercelliData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Vivek GandhiData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Kathleen GondekData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Ajit A LondheCenter for Observational Research, Amgen, Inc, Thousand Oaks, California, USA.ORCID 0000-0002-0338-2919
Junjie MaCenter for Observational Research, Amgen, Inc, Thousand Oaks, California, USA.ORCID 0000-0002-6249-876X
Michele Jonsson-FunkDepartment of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA.
Hillary A KeenanData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Sudhakar ManneData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Kaili RenData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Lynn SandersData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Peter YuData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Jie ZhangCenter for Observational Research, Amgen, Inc, Thousand Oaks, California, USA.
Linyun ZhouData Sciences Institute, Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA.
Ying BaoCenter for Observational Research and Data Science, Bristol-Myers Squibb, Princeton, New Jersey, USA ying.bao@bms.com.
Takeda (United States) · USAmgen (United States) · USBristol-Myers Squibb (United States) · USUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo examine age, gender, and temporal differences in baseline characteristics and clinical outcomes of adult patients hospitalised with COVID-19.

designA cohort study using deidentified electronic medical records from a Global Research Network. SETTING/

participants67 456 adult patients hospitalised with COVID-19 from the USA; 7306 from Europe, Latin America and Asia-Pacific between February 2020 and January 2021.

resultsIn the US cohort, compared with patients 18-34 years old, patients ≥65 had a greater risk of intensive care unit (ICU) admission (adjusted HR (aHR) 1.73, 95% CI 1.58 to 1.90), acute respiratory distress syndrome(ARDS)/respiratory failure (aHR 1.86, 95% CI 1.76 to 1.96), invasive mechanical ventilation (IMV, aHR 1.93, 95% CI, 1.73 to 2.15), and all-cause mortality (aHR 5.6, 95% CI 4.36 to 7.18). Men appeared to be at a greater risk for ICU admission (aHR 1.34, 95% CI 1.29 to 1.39), ARDS/respiratory failure (aHR 1.24, 95% CI1.21 to 1.27), IMV (aHR 1.38, 95% CI 1.32 to 1.45), and all-cause mortality (aHR 1.16, 95% CI 1.08 to 1.24) compared with women. Moreover, we observed a greater risk of adverse outcomes during the early pandemic (ie, February-April 2020) compared with later periods. In the ex-US cohort, the age and gender trends were similar; for the temporal trend, the highest proportion of patients with all-cause mortality were also in February-April 2020; however, the highest percentages of patients with IMV and ARDS/respiratory failure were in August-October 2020 followed by February-April 2020.

conclusionsThis study provided valuable information on the temporal trends of characteristics and outcomes of hospitalised adult COVID-19 patients in both USA and ex-USA. It also described the population at a potentially greater risk for worse clinical outcomes by identifying the age and gender differences. Together, the information could inform the prevention and treatment strategies of COVID-19. Furthermore, it can be used to raise public awareness of COVID-19's impact on vulnerable populations.

Indexed as

COVID-19AdolescentAdultCohort StudiesFemaleGlobal HealthHospitalizationHumansIntensive Care UnitsMalePandemicsRespiration, ArtificialSARS-CoV-2Young AdultCOVID-19epidemiologyinfectious diseasespublic health

Identifiers

PMID34362806
PMCPMC8350974
OpenAlexW3189080269

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.