Evidence map›Paper›PMID 33683539›Full record

ArticleInternal and emergency medicine2022

Emergency room comprehensive assessment of demographic, radiological, laboratory and clinical data of patients with COVID-19: determination of its prognostic value for in-hospital mortality.

Marco Gatti, Marco Calandri, Andrea Biondo, Carlotta Geninatti, Clara Piatti, Irene Ruggirello, Ambra Santonocito, Sara Varello, Laura Bergamasco, Paolo Bironzo and 10 more

Open access · hybridAbstract read
In one paragraph

Article in Internal and emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
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  6. Article
  7. Nucleic Acid Testing of SARS-CoV-2.International journal of molecular sciences · 2021
    Review
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

20 authors at 4 institutions in 2 countries.

Marco GattiDepartment of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy. marcogatti17@gmail.com.ORCID http://orcid.org/0000-0001-8168-5280
Marco CalandriRadiology Department A.O.U. San Luigi Gonzaga, Regione Gonzole 10, Orbassano, Italy.
Andrea BiondoDepartment of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy.
Carlotta GeninattiRadiology Department A.O.U. San Luigi Gonzaga, Regione Gonzole 10, Orbassano, Italy.
Clara PiattiRadiology Department A.O.U. San Luigi Gonzaga, Regione Gonzole 10, Orbassano, Italy.
Irene RuggirelloDepartment of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy.
Ambra SantonocitoDepartment of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy.
Sara VarelloDepartment of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy.
Laura BergamascoDepartment of Surgical Sciences, University of Turin, Turin, Italy.
Paolo BironzoDepartment of Oncology, University of Turin, Turin, Italy.
Adriana BoccuzziEmergency Department, San Luigi Gonzaga University Hospital, Orbassano, TO, Italy.
Luca BrazziDepartment of Surgical Sciences, Anesthesia Unit, University of Turin, Turin, Italy.
Pietro CaironiDepartment of Oncology, University of Turin, Turin, Italy.
Luciano CardinaleRadiology Department A.O.U. San Luigi Gonzaga, Regione Gonzole 10, Orbassano, Italy.
Rossana CavalloDepartment of Public Health and Pediatrics, Laboratory of Microbiology and Virology, Città della Salute e della Scienza Hospital, University of Turin, Turin, Italy.
Franco RiccardiniDepartment of Medical Science, University of Turin, Turin, Italy.
Giorgio LimeruttiDepartment of Radiology, S.C. Radiodiagnostica Ospedaliera, Turin, Italy.
Andrea VeltriRadiology Department A.O.U. San Luigi Gonzaga, Regione Gonzole 10, Orbassano, Italy.
Paolo FonioDepartment of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy.
Riccardo FalettiDepartment of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy.
University of Turin · ITOspedale San Luigi Gonzaga · ITDepartment of Medical Sciences · BYOspedale Sant'Anna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mortality risk in COVID-19 patients is determined by several factors. The aim of our study was to adopt an integrated approach based on clinical, laboratory and chest x-ray (CXR) findings collected at the patient's admission to Emergency Room (ER) to identify prognostic factors. Retrospective study on 346 consecutive patients admitted to the ER of two North-Western Italy hospitals between March 9 and April 10, 2020 with clinical suspicion of COVID-19 confirmed by reverse transcriptase-polymerase reaction chain test (RT-PCR), CXR performed within 24 h (analyzed with two different scores) and recorded prognosis. Clinical and laboratory data were collected. Statistical analysis on the features of 83 in-hospital dead vs 263 recovered patients was performed with univariate (uBLR), multivariate binary logistic regression (mBLR) and ROC curve analysis. uBLR identified significant differences for several variables, most of them intertwined by multiple correlations. mBLR recognized as significant independent predictors for in-hospital mortality age > 75 years, C-reactive protein (CRP) > 60 mg/L, PaO

Indexed as

COVID-19AgedEmergency Service, HospitalHospital MortalityHumansLaboratoriesPrognosisRadiography, ThoracicRetrospective StudiesSARS-CoV-2Chest X-rayCoronavirus disease 2019 (COVID-19)OutcomePrognosisSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Identifiers

PMID33683539
PMCPMC7938271
OpenAlexW3135670543

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.