Early biochemical analysis of COVID-19 patients helps severity prediction
Copyright: © 2023 Roncancio-Clavijo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
PloS one - 18(2023), 5 vom: 29., Seite e0283469 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Roncancio-Clavijo, Andrés [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 22.05.2023 Date Revised 05.06.2023 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0283469 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright: © 2023 Roncancio-Clavijo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | ||
| 520 | |a COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease | ||
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| 700 | 1 | |a Alberro, Ainhoa |e verfasserin |4 aut | |
| 700 | 1 | |a Iribarren-Lopez, Andrea |e verfasserin |4 aut | |
| 700 | 1 | |a Butler, Ray |e verfasserin |4 aut | |
| 700 | 1 | |a Lopez, Raúl |e verfasserin |4 aut | |
| 700 | 1 | |a Iribarren, Jose Antonio |e verfasserin |4 aut | |
| 700 | 1 | |a Clemente, Diego |e verfasserin |4 aut | |
| 700 | 1 | |a Marimon, Jose María |e verfasserin |4 aut | |
| 700 | 1 | |a Basterrechea, Javier |e verfasserin |4 aut | |
| 700 | 1 | |a Martinez, Bruno |e verfasserin |4 aut | |
| 700 | 1 | |a Prada, Alvaro |e verfasserin |4 aut | |
| 700 | 1 | |a Otaegui, David |e verfasserin |4 aut | |
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