Evidence map›Paper›PMID 33999930›Full record

ArticlePloS one2021

Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients.

Daniel Garzon-Chavez, Daniel Romero-Alvarez, Marco Bonifaz, Juan Gaviria, Daniel Mero, Narcisa Gunsha, Asiris Perez, María Garcia, Hugo Espejo, Franklin Espinosa and 6 more

Abstract 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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  4. Review
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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

16 authors.

Daniel Garzon-ChavezColegio de Ciencias de la Salud, Universidad San Francisco de Quito, Quito, Ecuador.ORCID 0000-0002-8024-3509
Daniel Romero-AlvarezBiodiversity Institute and Department of Ecology & Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America.
Marco BonifazHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Juan GaviriaHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Daniel MeroHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Narcisa GunshaHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Asiris PerezHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
María GarciaHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Hugo EspejoHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Franklin EspinosaHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.ORCID 0000-0002-9905-1785
Edison LigñaHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Mauricio EspinelDireccion de Salud Individual y Familiar, Instituto Ecuatoriano de Seguridad Social (IESS), Quito, Ecuador.
Emmanuelle QuentinCentro de Investigación en Salud Pública y Epidemiología Clínica (CISPEC), Universidad Tecnológica Equinoccial, Quito, Ecuador.ORCID 0000-0001-5600-2361
Enrique TeranColegio de Ciencias de la Salud, Universidad San Francisco de Quito, Quito, Ecuador.ORCID 0000-0001-6979-5655
Francisco MoraHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.
Jorge ReyesHospital del Instituto Ecuatoriano de Seguridad Social (IESS) Quito-Sur, Quito, Ecuador.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The World Health Organization (WHO) declared coronavirus disease-2019 (COVID-19) a global pandemic on 11 March 2020. In Ecuador, the first case of COVID-19 was recorded on 29 February 2020. Despite efforts to control its spread, SARS-CoV-2 overran the Ecuadorian public health system, which became one of the most affected in Latin America on 24 April 2020. The Hospital General del Sur de Quito (HGSQ) had to transition from a general to a specific COVID-19 health center in a short period of time to fulfill the health demand from patients with respiratory afflictions. Here, we summarized the implementations applied in the HGSQ to become a COVID-19 exclusive hospital, including the rearrangement of hospital rooms and a triage strategy based on a severity score calculated through an artificial intelligence (AI)-assisted chest computed tomography (CT). Moreover, we present clinical, epidemiological, and laboratory data from 75 laboratory tested COVID-19 patients, which represent the first outbreak of Quito city. The majority of patients were male with a median age of 50 years. We found differences in laboratory parameters between intensive care unit (ICU) and non-ICU cases considering C-reactive protein, lactate dehydrogenase, and lymphocytes. Sensitivity and specificity of the AI-assisted chest CT were 21.4% and 66.7%, respectively, when considering a score >70%; regardless, this system became a cornerstone of hospital triage due to the lack of RT-PCR testing and timely results. If health workers act as vectors of SARS-CoV-2 at their domiciles, they can seed outbreaks that might put 1,879,047 people at risk of infection within 15 km around the hospital. Despite our limited sample size, the information presented can be used as a local example that might aid future responses in low and middle-income countries facing respiratory transmitted epidemics.

Indexed as

AdultAgedArtificial IntelligenceCOVID-19COVID-19 Nucleic Acid TestingEcuadorFemaleHospitals, SpecialHumansIntensive Care UnitsMaleMass Chest X-RayMiddle AgedPandemicsReal-Time Polymerase Chain ReactionReverse Transcriptase Polymerase Chain Reaction

Identifiers

PMID33999930
PMCPMC8128267

What OpenQuestion holds

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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.