Evidence map›Paper›PMID 34441957›Full record

ArticleJournal of clinical medicine2021

Usefulness of Easy-to-Use Risk Scoring Systems Rated in the Emergency Department to Predict Major Adverse Outcomes in Hospitalized COVID-19 Patients.

Julieta González-Flores, Carlos García-Ávila, Rashidi Springall, Malinalli Brianza-Padilla, Yaneli Juárez-Vicuña, Ricardo Márquez-Velasco, Fausto Sánchez-Muñoz, Martha A Ballinas-Verdugo, Edna Basilio-Gálvez, Mauricio Castillo-Salazar and 9 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 34% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

19 authors at 2 institutions in 1 country.

Julieta González-FloresImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.ORCID 0000-0002-7329-3751
Carlos García-ÁvilaImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.ORCID 0000-0003-3388-2006
Rashidi SpringallImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Malinalli Brianza-PadillaImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Yaneli Juárez-VicuñaImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Ricardo Márquez-VelascoImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Fausto Sánchez-MuñozImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.ORCID 0000-0001-6556-1632
Martha A Ballinas-VerdugoImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Edna Basilio-GálvezImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Mauricio Castillo-SalazarImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Sergio Cásarez-AlvaradoImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Adrián Hernández-DiazcouderImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.ORCID 0000-0001-6569-2851
José L Sánchez-GloriaImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Julio SandovalImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.ORCID 0000-0001-6825-3600
Héctor González-PachecoCoronary Care Unit, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.ORCID 0000-0002-4370-9963
Claudia Tavera-AlonsoCore Lab, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Gustavo Rojas-VelascoCardiovascular Intensive Care Unit, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Francisco Baranda-TovarCardiovascular Intensive Care Unit, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.
Luis M Amezcua-GuerraImmunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Tlalpan, Mexico City 14080, Mexico.ORCID 0000-0002-6258-5732
Instituto Nacional de Cardiología · MXUniversidad Autónoma Metropolitana · MX

Funding

CSRD VA 1
6 · The paper itself

Abstract

backgroundSeveral easy-to-use risk scoring systems have been built to identify patients at risk of developing complications associated with COVID-19. However, information about the ability of each score to early predict major adverse outcomes during hospitalization of severe COVID-19 patients is still scarce.

methodsEight risk scoring systems were rated upon arrival at the Emergency Department, and the occurrence of thrombosis, need for mechanical ventilation, death, and a composite that included all major adverse outcomes were assessed during the hospital stay. The clinical performance of each risk scoring system was evaluated to predict each major outcome. Finally, the diagnostic characteristics of the risk scoring system that showed the best performance for each major outcome were obtained.

resultsOne hundred and fifty-seven adult patients (55 ± 12 years, 66% men) were assessed at admission to the Emergency Department and included in the study. A total of 96 patients (61%) had at least one major outcome during hospitalization; 32 had thrombosis (20%), 80 required mechanical ventilation (50%), and 52 eventually died (33%). Of all the scores, Obesity and Diabetes (based on a history of comorbid conditions) showed the best performance for predicting mechanical ventilation (area under the ROC curve (AUC), 0.96; positive likelihood ratio (LR+), 23.7), death (AUC, 0.86; LR+, 4.6), and the composite outcome (AUC, 0.89; LR+, 15.6). Meanwhile, the inflammation-based risk scoring system (including leukocyte count, albumin, and C-reactive protein levels) was the best at predicting thrombosis (AUC, 0.63; LR+, 2.0).

conclusionsBoth the Obesity and Diabetes score and the inflammation-based risk scoring system appeared to be efficient enough to be integrated into the evaluation of COVID-19 patients upon arrival at the Emergency Department.

Indexed as

COVID-19diabetesinflammationmechanical ventilationobesitythrombosis

Identifiers

PMID34441957
PMCPMC8397140
OpenAlexW3193587219

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.