Cargando…

A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score

BACKGROUND: While several studies aimed to identify risk factors for severe COVID-19 cases to better anticipate intensive care unit admissions, very few have been conducted on self-reported patient symptoms and characteristics, predictive of RT-PCR test positivity. We therefore aimed to identify tho...

Descripción completa

Detalles Bibliográficos
Autores principales: Diaz Badial, Pablo, Bothorel, Hugo, Kherad, Omar, Dussoix, Philippe, Tallonneau Bory, Faustine, Ramlawi, Majd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867452/
https://www.ncbi.nlm.nih.gov/pubmed/35209872
http://dx.doi.org/10.1186/s12879-022-07164-1
_version_ 1784656056439275520
author Diaz Badial, Pablo
Bothorel, Hugo
Kherad, Omar
Dussoix, Philippe
Tallonneau Bory, Faustine
Ramlawi, Majd
author_facet Diaz Badial, Pablo
Bothorel, Hugo
Kherad, Omar
Dussoix, Philippe
Tallonneau Bory, Faustine
Ramlawi, Majd
author_sort Diaz Badial, Pablo
collection PubMed
description BACKGROUND: While several studies aimed to identify risk factors for severe COVID-19 cases to better anticipate intensive care unit admissions, very few have been conducted on self-reported patient symptoms and characteristics, predictive of RT-PCR test positivity. We therefore aimed to identify those predictive factors and construct a predictive score for the screening of patients at admission. METHODS: This was a monocentric retrospective analysis of clinical data from 9081 patients tested for SARS-CoV-2 infection from August 1 to November 30 2020. A multivariable logistic regression using least absolute shrinkage and selection operator (LASSO) was performed on a training dataset (60% of the data) to determine associations between self-reported patient characteristics and COVID-19 diagnosis. Regression coefficients were used to construct the Coronavirus 2019 Identification score (COV(19)-ID) and the optimal threshold calculated on the validation dataset (20%). Its predictive performance was finally evaluated on a test dataset (20%). RESULTS: A total of 2084 (22.9%) patients were tested positive to SARS-CoV-2 infection. Using the LASSO model, COVID-19 was independently associated with loss of smell (Odds Ratio, 6.4), fever (OR, 2.7), history of contact with an infected person (OR, 1.7), loss of taste (OR, 1.5), muscle stiffness (OR, 1.5), cough (OR, 1.5), back pain (OR, 1.4), loss of appetite (OR, 1.3), as well as male sex (OR, 1.05). Conversely, COVID-19 was less likely associated with smoking (OR, 0.5), sore throat (OR, 0.9) and ear pain (OR, 0.9). All aforementioned variables were included in the COV(19)-ID score, which demonstrated on the test dataset an area under the receiver-operating characteristic curve of 82.9% (95% CI 80.6%–84.9%), and an accuracy of 74.2% (95% CI 74.1%–74.3%) with a high sensitivity (80.4%, 95% CI [80.3%–80.6%]) and specificity (72.2%, 95% CI [72.2%–72.4%]). CONCLUSIONS: The COV(19)-ID score could be useful in early triage of patients needing RT-PCR testing thus alleviating the burden on laboratories, emergency rooms, and wards. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07164-1.
format Online
Article
Text
id pubmed-8867452
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-88674522022-02-24 A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score Diaz Badial, Pablo Bothorel, Hugo Kherad, Omar Dussoix, Philippe Tallonneau Bory, Faustine Ramlawi, Majd BMC Infect Dis Research BACKGROUND: While several studies aimed to identify risk factors for severe COVID-19 cases to better anticipate intensive care unit admissions, very few have been conducted on self-reported patient symptoms and characteristics, predictive of RT-PCR test positivity. We therefore aimed to identify those predictive factors and construct a predictive score for the screening of patients at admission. METHODS: This was a monocentric retrospective analysis of clinical data from 9081 patients tested for SARS-CoV-2 infection from August 1 to November 30 2020. A multivariable logistic regression using least absolute shrinkage and selection operator (LASSO) was performed on a training dataset (60% of the data) to determine associations between self-reported patient characteristics and COVID-19 diagnosis. Regression coefficients were used to construct the Coronavirus 2019 Identification score (COV(19)-ID) and the optimal threshold calculated on the validation dataset (20%). Its predictive performance was finally evaluated on a test dataset (20%). RESULTS: A total of 2084 (22.9%) patients were tested positive to SARS-CoV-2 infection. Using the LASSO model, COVID-19 was independently associated with loss of smell (Odds Ratio, 6.4), fever (OR, 2.7), history of contact with an infected person (OR, 1.7), loss of taste (OR, 1.5), muscle stiffness (OR, 1.5), cough (OR, 1.5), back pain (OR, 1.4), loss of appetite (OR, 1.3), as well as male sex (OR, 1.05). Conversely, COVID-19 was less likely associated with smoking (OR, 0.5), sore throat (OR, 0.9) and ear pain (OR, 0.9). All aforementioned variables were included in the COV(19)-ID score, which demonstrated on the test dataset an area under the receiver-operating characteristic curve of 82.9% (95% CI 80.6%–84.9%), and an accuracy of 74.2% (95% CI 74.1%–74.3%) with a high sensitivity (80.4%, 95% CI [80.3%–80.6%]) and specificity (72.2%, 95% CI [72.2%–72.4%]). CONCLUSIONS: The COV(19)-ID score could be useful in early triage of patients needing RT-PCR testing thus alleviating the burden on laboratories, emergency rooms, and wards. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07164-1. BioMed Central 2022-02-24 /pmc/articles/PMC8867452/ /pubmed/35209872 http://dx.doi.org/10.1186/s12879-022-07164-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Diaz Badial, Pablo
Bothorel, Hugo
Kherad, Omar
Dussoix, Philippe
Tallonneau Bory, Faustine
Ramlawi, Majd
A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score
title A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score
title_full A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score
title_fullStr A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score
title_full_unstemmed A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score
title_short A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV(19)-ID score
title_sort new screening tool for sars-cov-2 infection based on self-reported patient clinical characteristics: the cov(19)-id score
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867452/
https://www.ncbi.nlm.nih.gov/pubmed/35209872
http://dx.doi.org/10.1186/s12879-022-07164-1
work_keys_str_mv AT diazbadialpablo anewscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT bothorelhugo anewscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT kheradomar anewscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT dussoixphilippe anewscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT tallonneauboryfaustine anewscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT ramlawimajd anewscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT diazbadialpablo newscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT bothorelhugo newscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT kheradomar newscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT dussoixphilippe newscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT tallonneauboryfaustine newscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore
AT ramlawimajd newscreeningtoolforsarscov2infectionbasedonselfreportedpatientclinicalcharacteristicsthecov19idscore