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The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients
Deterioration is sometimes unexpected in SARS-CoV2 infection. The aim of our study is to establish laboratory predictors of mortality in COVID-19 disease which can help to identify high risk patients. All patients admitted to hospital due to Covid-19 disease were included. Laboratory biomarkers that...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087839/ https://www.ncbi.nlm.nih.gov/pubmed/33931677 http://dx.doi.org/10.1038/s41598-021-88679-6 |
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author | Antunez Muiños, Pablo Jose López Otero, Diego Amat-Santos, Ignacio J. López País, Javier Aparisi, Alvaro Cacho Antonio, Carla E. Catalá, Pablo González Ferrero, Teba Cabezón, Gonzalo Otero García, Oscar Gil, José Francisco Pérez Poza, Marta Candela, Jordi Rojas, Gino Jiménez Ramos, Víctor Veras, Carlos San Román, J. Alberto González-Juanatey, José R. |
author_facet | Antunez Muiños, Pablo Jose López Otero, Diego Amat-Santos, Ignacio J. López País, Javier Aparisi, Alvaro Cacho Antonio, Carla E. Catalá, Pablo González Ferrero, Teba Cabezón, Gonzalo Otero García, Oscar Gil, José Francisco Pérez Poza, Marta Candela, Jordi Rojas, Gino Jiménez Ramos, Víctor Veras, Carlos San Román, J. Alberto González-Juanatey, José R. |
author_sort | Antunez Muiños, Pablo Jose |
collection | PubMed |
description | Deterioration is sometimes unexpected in SARS-CoV2 infection. The aim of our study is to establish laboratory predictors of mortality in COVID-19 disease which can help to identify high risk patients. All patients admitted to hospital due to Covid-19 disease were included. Laboratory biomarkers that contributed with significant predictive value for predicting mortality to the clinical model were included. Cut-off points were established, and finally a risk score was built. 893 patients were included. Median age was 68.2 ± 15.2 years. 87(9.7%) were admitted to Intensive Care Unit (ICU) and 72(8.1%) needed mechanical ventilation support. 171(19.1%) patients died. A Covid-19 Lab score ranging from 0 to 30 points was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included haemoglobin, erythrocytes, leukocytes, neutrophils, lymphocytes, creatinine, C-reactive protein, interleukin-6, procalcitonin, lactate dehydrogenase (LDH), and D-dimer. Three groups were established. Low mortality risk group under 12 points, 12 to 18 were included as moderate risk, and high risk group were those with 19 or more points. Low risk group as reference, moderate and high patients showed mortality OR 4.75(CI95% 2.60–8.68) and 23.86(CI 95% 13.61–41.84), respectively. C-statistic was 0–85(0.82–0.88) and Hosmer–Lemeshow p-value 0.63. Covid-19 Lab score can very easily predict mortality in patients at any moment during admission secondary to SARS-CoV2 infection. It is a simple and dynamic score, and it can be very easily replicated. It could help physicians to identify high risk patients to foresee clinical deterioration. |
format | Online Article Text |
id | pubmed-8087839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80878392021-05-03 The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients Antunez Muiños, Pablo Jose López Otero, Diego Amat-Santos, Ignacio J. López País, Javier Aparisi, Alvaro Cacho Antonio, Carla E. Catalá, Pablo González Ferrero, Teba Cabezón, Gonzalo Otero García, Oscar Gil, José Francisco Pérez Poza, Marta Candela, Jordi Rojas, Gino Jiménez Ramos, Víctor Veras, Carlos San Román, J. Alberto González-Juanatey, José R. Sci Rep Article Deterioration is sometimes unexpected in SARS-CoV2 infection. The aim of our study is to establish laboratory predictors of mortality in COVID-19 disease which can help to identify high risk patients. All patients admitted to hospital due to Covid-19 disease were included. Laboratory biomarkers that contributed with significant predictive value for predicting mortality to the clinical model were included. Cut-off points were established, and finally a risk score was built. 893 patients were included. Median age was 68.2 ± 15.2 years. 87(9.7%) were admitted to Intensive Care Unit (ICU) and 72(8.1%) needed mechanical ventilation support. 171(19.1%) patients died. A Covid-19 Lab score ranging from 0 to 30 points was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included haemoglobin, erythrocytes, leukocytes, neutrophils, lymphocytes, creatinine, C-reactive protein, interleukin-6, procalcitonin, lactate dehydrogenase (LDH), and D-dimer. Three groups were established. Low mortality risk group under 12 points, 12 to 18 were included as moderate risk, and high risk group were those with 19 or more points. Low risk group as reference, moderate and high patients showed mortality OR 4.75(CI95% 2.60–8.68) and 23.86(CI 95% 13.61–41.84), respectively. C-statistic was 0–85(0.82–0.88) and Hosmer–Lemeshow p-value 0.63. Covid-19 Lab score can very easily predict mortality in patients at any moment during admission secondary to SARS-CoV2 infection. It is a simple and dynamic score, and it can be very easily replicated. It could help physicians to identify high risk patients to foresee clinical deterioration. Nature Publishing Group UK 2021-04-30 /pmc/articles/PMC8087839/ /pubmed/33931677 http://dx.doi.org/10.1038/s41598-021-88679-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . |
spellingShingle | Article Antunez Muiños, Pablo Jose López Otero, Diego Amat-Santos, Ignacio J. López País, Javier Aparisi, Alvaro Cacho Antonio, Carla E. Catalá, Pablo González Ferrero, Teba Cabezón, Gonzalo Otero García, Oscar Gil, José Francisco Pérez Poza, Marta Candela, Jordi Rojas, Gino Jiménez Ramos, Víctor Veras, Carlos San Román, J. Alberto González-Juanatey, José R. The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients |
title | The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients |
title_full | The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients |
title_fullStr | The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients |
title_full_unstemmed | The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients |
title_short | The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients |
title_sort | covid-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in covid-19 patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087839/ https://www.ncbi.nlm.nih.gov/pubmed/33931677 http://dx.doi.org/10.1038/s41598-021-88679-6 |
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