Evidence map›Paper›PMID 34614008›Full record

ArticlePloS one2021

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes.

Stephen Salerno, Yuming Sun, Emily L Morris, Xinwei He, Yajing Li, Ziyang Pan, Peisong Han, Jian Kang, Michael W Sjoding, Yi Li

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

10 citing papers in PubMed, 18 citations in OpenAlex.

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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

10 authors at 1 institution in 1 country.

Stephen SalernoDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.ORCID 0000-0003-2763-0494
Yuming SunDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
Emily L MorrisDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
Xinwei HeDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
Yajing LiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
Ziyang PanDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.ORCID 0000-0003-1600-5597
Peisong HanDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
Jian KangDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
Michael W SjodingDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States of America.
Yi LiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
University of Michigan · US

Funding

New Statistical Methods for Modelling Cancer OutcomesR01CA249096 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yi Li · 2021 to 2026
$2.5M
Data-Driven Identification of the Acute Respiratory Distress SyndromeK01HL136687 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SJODING, MICHAEL WILLIAM · 2017 to 2021
$862k
NCI NIH HHS R01 CA249096NHLBI NIH HHS K01 HL136687
6 · The paper itself

Abstract

backgroundUnderstanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories.

methodsThis was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions.

resultsYounger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates.

conclusionsThis study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.

Indexed as

COVID-19Healthcare DisparitiesAdultAgedEpidemicsEthnicityFacilities and Services UtilizationFemaleHealth ResourcesHumansLung DiseasesMaleMichiganMiddle AgedRetrospective StudiesRisk Factors

Identifiers

PMID34614008
PMCPMC8494298
OpenAlexW3204582052

What OpenQuestion holds

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LicenceCC BY
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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.