Early biochemical analysis of COVID-19 patients helps severity prediction.
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,...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0283469 |
| _version_ | 1797821491341950976 |
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| author | Andrés Roncancio-Clavijo Miriam Gorostidi-Aicua Ainhoa Alberro Andrea Iribarren-Lopez Ray Butler Raúl Lopez Jose Antonio Iribarren Diego Clemente Jose María Marimon Javier Basterrechea Bruno Martinez Alvaro Prada David Otaegui |
| author_facet | Andrés Roncancio-Clavijo Miriam Gorostidi-Aicua Ainhoa Alberro Andrea Iribarren-Lopez Ray Butler Raúl Lopez Jose Antonio Iribarren Diego Clemente Jose María Marimon Javier Basterrechea Bruno Martinez Alvaro Prada David Otaegui |
| author_sort | Andrés Roncancio-Clavijo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T09:53:27Z |
| format | Article |
| id | doaj.art-4b422684220a4a058f3bfc1cfe219670 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T09:53:27Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4b422684220a4a058f3bfc1cfe2196702023-05-24T05:31:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01185e028346910.1371/journal.pone.0283469Early biochemical analysis of COVID-19 patients helps severity prediction.Andrés Roncancio-ClavijoMiriam Gorostidi-AicuaAinhoa AlberroAndrea Iribarren-LopezRay ButlerRaúl LopezJose Antonio IribarrenDiego ClementeJose María MarimonJavier BasterrecheaBruno MartinezAlvaro PradaDavid OtaeguiCOVID-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.https://doi.org/10.1371/journal.pone.0283469 |
| spellingShingle | Andrés Roncancio-Clavijo Miriam Gorostidi-Aicua Ainhoa Alberro Andrea Iribarren-Lopez Ray Butler Raúl Lopez Jose Antonio Iribarren Diego Clemente Jose María Marimon Javier Basterrechea Bruno Martinez Alvaro Prada David Otaegui Early biochemical analysis of COVID-19 patients helps severity prediction. PLoS ONE |
| title | Early biochemical analysis of COVID-19 patients helps severity prediction. |
| title_full | Early biochemical analysis of COVID-19 patients helps severity prediction. |
| title_fullStr | Early biochemical analysis of COVID-19 patients helps severity prediction. |
| title_full_unstemmed | Early biochemical analysis of COVID-19 patients helps severity prediction. |
| title_short | Early biochemical analysis of COVID-19 patients helps severity prediction. |
| title_sort | early biochemical analysis of covid 19 patients helps severity prediction |
| url | https://doi.org/10.1371/journal.pone.0283469 |
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