Evidence map›Paper›PMID 34143791›Full record

ArticlePloS one2021

Predictors of severity and mortality among patients hospitalized with COVID-19 in Rhode Island.

Aakriti Pandita, Fizza S Gillani, Yiyun Shi, Anna Hardesty, Meghan McCarthy, Jad Aridi, Dimitrios Farmakiotis, Silvia S Chiang, Curt G Beckwith

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 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 8% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 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

9 authors at 2 institutions in 1 country.

Aakriti PanditaDepartment of Medicine, University of Colorado School of Medicine, Denver, Colorado, United States of America.ORCID 0000-0002-5591-2328
Fizza S GillaniDivision of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Yiyun ShiDepartment of Internal Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Anna HardestyDepartment of Internal Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Meghan McCarthyDivision of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Jad AridiDivision of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Dimitrios FarmakiotisDivision of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Silvia S ChiangDepartment of Pediatrics, Division of Pediatric Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Curt G BeckwithDivision of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Brown University · USUniversity of Colorado Denver · US

Funding

Translational ScienceP30AI042853 · NIAID · MIRIAM HOSPITAL · PI CURT G BECKWITH · 1998 to 2026
$51.7M
NIAID NIH HHS P30 AI042853
6 · The paper itself

Abstract

backgroundIn order for healthcare systems to prepare for future waves of COVID-19, an in-depth understanding of clinical predictors is essential for efficient triage of hospitalized patients.

methodsWe performed a retrospective cohort study of 259 patients admitted to our hospitals in Rhode Island to examine differences in baseline characteristics (demographics and comorbidities) as well as presenting symptoms, signs, labs, and imaging findings that predicted disease progression and in-hospital mortality.

resultsPatients with severe COVID-19 were more likely to be older (p = 0.02), Black (47.2% vs. 32.0%, p = 0.04), admitted from a nursing facility (33.0% vs. 17.9%, p = 0.006), have diabetes (53.9% vs. 30.4%, p<0.001), or have COPD (15.4% vs. 6.6%, p = 0.02). In multivariate regression, Black race (adjusted odds ratio [aOR] 2.0, 95% confidence interval [CI]: 1.1-3.9) and diabetes (aOR 2.2, 95%CI: 1.3-3.9) were independent predictors of severe disease, while older age (aOR 1.04, 95% CI: 1.01-1.07), admission from a nursing facility (aOR 2.7, 95% CI 1.1-6.7), and hematological co-morbidities predicted mortality (aOR 3.4, 95% CI 1.1-10.0). In the first 24 hours, respiratory symptoms (aOR 7.0, 95% CI: 1.4-34.1), hypoxia (aOR 19.9, 95% CI: 2.6-152.5), and hypotension (aOR 2.7, 95% CI) predicted progression to severe disease, while tachypnea (aOR 8.7, 95% CI: 1.1-71.7) and hypotension (aOR 9.0, 95% CI: 3.1-26.1) were associated with increased in-hospital mortality.

conclusionsCertain patient characteristics and clinical features can help clinicians with early identification and triage of high-risk patients during subsequent waves of COVID-19.

Indexed as

Hospital MortalitySeverity of Illness IndexAgedComorbidityCOVID-19Diabetes MellitusEpidemicsFemaleHospitalizationHumansHypotensionMaleMiddle AgedRetrospective StudiesRhode IslandRisk Factors

Identifiers

PMID34143791
PMCPMC8213072
OpenAlexW3169222793

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.