Evidence map›Paper›PMID 40281203›Full record

ArticleCommunications medicine2025

Dissecting clinical features of COVID-19 in a cohort of 21,312 acute care patients.

Cole Maguire, Elie Soloveichik, Netta Blinchevsky, Jaimie Miller, Robert Morrison, Johanna Busch, W Michael Brode, Dennis Wylie, Justin Rousseau, Esther Melamed

Abstract read
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Cole MaguireDepartment of Neurology, The University of Texas at, Austin, Dell Medical School, Austin, TX, USA.ORCID http://orcid.org/0000-0001-8986-9762
Elie SoloveichikDepartment of Neurology, The University of Texas at, Austin, Dell Medical School, Austin, TX, USA.
Netta BlinchevskyDepartment of Neurology, The University of Texas at, Austin, Dell Medical School, Austin, TX, USA.
Jaimie MillerEnterprise Data Intelligence, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.
Robert MorrisonDepartment of Internal Medicine, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.
Johanna BuschDepartment of Internal Medicine, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.ORCID http://orcid.org/0000-0003-3334-0768
W Michael BrodeDepartment of Internal Medicine, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.ORCID http://orcid.org/0009-0007-0930-478X
Dennis WylieCenter for Biomedical Support, The University of Texas at Austin, Austin, TX, USA.
Justin RousseauDepartment of Neurology, The University of Texas at, Austin, Dell Medical School, Austin, TX, USA.ORCID http://orcid.org/0000-0002-2817-9124
Esther MelamedDepartment of Neurology, The University of Texas at, Austin, Dell Medical School, Austin, TX, USA. esther.melamed@austin.utexas.edu.ORCID http://orcid.org/0000-0001-5571-3591

Funding

The contribution of GPCRs to thymocyte medullary entry and central toleranceR01AI104870 · NIAID · UNIVERSITY OF TEXAS AT AUSTIN · PI Lauren Ilyse Richie EHRLICH · 2014 to 2026
$7.6M
PRE-DOCTORAL TRAINING IN INTERDISCIPLINARY NEUROSCIENCET32DA018926 · NIDA · UNIVERSITY OF TEXAS AUSTIN · PI Nace L Golding, AMY LEE · 2004 to 2026
$5.6M
Alcohol's Impact on the Gut-Brain Axis in a Mouse Model of Multiple SclerosisK08AA027837 · NIAAA · UNIVERSITY OF TEXAS AT AUSTIN · PI MELAMED, ESTHER · 2020 to 2024
$971k
NIAAA NIH HHS K08 AA027837NIAID NIH HHS R01 AI104870NIDA NIH HHS T32 DA018926U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) R01AI104870-S1U.S. Department of Health & Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA) K08 T26-1616-11U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA) 5T32DA018926-18
6 · The paper itself

Abstract

backgroundAlthough, COVID-19 has resulted in over 7 million deaths globally, many questions still remain about the risk factors for disease severity and the effects of variants and vaccinations over the course of the pandemic. To address this gap, we conducted a retrospective analysis of electronic health records from COVID-19 patients over 2.5 years of the COVID-19 pandemic to identify associated clinical features.

methodsWe analyze a retrospective cohort of 21,312 acute-care patients over a 2.5 year period and define six clinical trajectory groups (TGs) associated with demographics, diagnoses, vitals, labs, imaging, consultations, and medications.

resultsWe show that the proportion of mild patients increased over time, particularly during Omicron waves. Additionally, while mild and fatal patients had differences in age, age did not distinguish patients with severe versus critical disease. Furthermore, we find that both male sex and Hispanic/Latino ethnicity are associated with more severe/critical TGs. More severe patients also have a higher rate of neuropsychiatric diagnoses and consultations, along with an immunological signature of high neutrophils and immature granulocytes, and low lymphocytes and monocytes. Interestingly, low albumin is one of the best lab predictors of COVID-19 severity in association with higher malnutrition in severe/critical patients, raising concern of nutritional insufficiency influencing COVID-19 outcomes. Despite this, only a small fraction of severe/critical patients had nutritional labs checked (e.g. Vitamin D, thiamine, B vitamins) or received vitamin supplementation.

conclusionsOur findings expand on clinical risk factors in COVID-19, and highlight the interaction between severity, nutritional status, and neuropsychiatric complications in acute care patients to enable identification of patients at risk for severe disease.

Identifiers

PMID40281203
PMCPMC12032146

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Registered trials

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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.