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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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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 |
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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 |
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