About: Development and validation of the quick COVID-19 severity index (qCSI): a prognostic tool for early clinical decompensation

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Development and validation of the quick COVID-19 severity index (qCSI): a prognostic tool for early clinical decompensation Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Development and validation of the quick COVID-19 severity index (qCSI): a prognostic tool for early clinical decompensation
Creator	Schulz, L Andrew Taylor, ; Haimovich, Adrian Patrick, ; Ravindra, Neal Schulz, Wade Taylor, R Taylor, Richard Van Dijk, David Stoytchev, Stoytcho Wade, ; David Van Dijk Phd2, ; Ms1, Stoytchev Patrick Young, H Perrywilson, Francis Perrywilson5, Francis Phd4, Young Ravindra Phd2, Neal Stoytcho, ;
source	Elsevier; PMC
abstract	Abstract Objective The goal of this study was to develop a prognostic tool of early hospital respiratory failure among emergency department (ED) patients admitted with COVID-19. Methods This was an observational, retrospective cohort study from a nine ED health system in the United States of admitted adult patients with SARS-CoV-2 (COVID-19) and a ≤ 6 L/min oxygen requirement. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of ≥ 10 L/min, any high-flow device, non-invasive or invasive ventilation, or death. Predictive models were compared to the Elixhauser comorbidity index, quick serial organ failure assessment (qSOFA), and the CURB-65 pneumonia severity score. Results During the study period from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. Using area under receiver-operating characteristic curves, we compared the performance of a novel bedside scoring system, the quick COVID-19 severity index (qCSI) composed of respiratory rate, oxygen saturation, and oxygen flow rate (mean [95% CI]) (0.81 [0.73-0.89]), a machine- learning model, the COVID-19 severity index (0.76 [0.65-0.86]), to the Elixhauser mortality index (0.61 [0.51-0.70])), CURB-65 (0.50 [0.40-0.60]), and qSOFA (0.59 [0.50-0.68]). A low qCSI score (≤ 3) had a sensitivity of 0.79 [0.65- 0.93] and specificity of 0.78 [0.72-0.83] in predicting respiratory decompensation with a less than 5% risk of outcome in the validation cohort. Conclusions A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted using bedside respiratory exam findings within a simple scoring system.
has issue date	2020-07-21(xsd:dateTime)
bibo:doi	10.1016/j.annemergmed.2020.07.022
has license	els-covid
sha1sum (hex)	7bf524449a8896cc34cf8f0eca12c3cf6cbde748
schema:url	https://doi.org/10.1016/j.annemergmed.2020.07.022
resource representing a document's title	Development and validation of the quick COVID-19 severity index (qCSI): a prognostic tool for early clinical decompensation
has PubMed Central identifier	PMC7373004
schema:publication	Ann Emerg Med
resource representing a document's body	covid:7bf524449a8896cc34cf8f0eca12c3cf6cbde748#body_text
is schema:about of	named entity 'tool' named entity 'decompensation' named entity 'COVID-19' named entity 'quick' named entity 'machine learning' named entity 'CXR' named entity 'laboratory tests' named entity 'variable selection' named entity 'pulse oximetry' named entity 'oxygen' named entity 'Elixhauser comorbidity index' named entity 'radiography' named entity 'CURB-65' named entity 'prognostic' named entity 'inter-rater agreement' named entity 'COVID-19' named entity 'model risk' named entity 'comorbidities' named entity 'respiratory illness' named entity 'COVID-19' named entity 'Bayesian optimization' named entity 'ferritin' named entity 'oxygen' named entity 'Elixhauser comorbidity index' named entity 'global pandemic' named entity 'COVID-19' named entity 'invasive ventilation' named entity 'cross-validation' named entity 'physiology' named entity 'respiratory rate' named entity 'higher risk' named entity 'ferritin' named entity 'oxygen' named entity 'game-theoretic' named entity 'COVID-19' named entity 'COVID-19' named entity 'Chest CT' named entity 'prognostication' named entity 'nasal cannula' named entity 'ng/mL' named entity 'levels of care' named entity 'CURB-65' named entity 'evidence-based' named entity 'SHAP' named entity 'CXR' named entity 'critical illness' named entity 'COVID-19' named entity 'healthcare providers' named entity 'two-sample t-test' named entity 'cross validation' named entity 'SHAP' named entity 'nasal cannula' named entity 'respiratory illness' named entity 'CXR' named entity 'organ failure' named entity 'tobacco' named entity 'SHAP' named entity 'logistic regression' named entity 'mechanical ventilation' named entity 'COVID-19' named entity 'alanine aminotransferase' named entity 'Oxygen' named entity 'risk stratification' named entity 'organ failure' named entity 'nucleic acid'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software