Early biochemical analysis of COVID-19 patients helps severity prediction

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 and David Otaegui

PLOS ONE, 2023, vol. 18, issue 5, 1-11

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0283469

DOI: 10.1371/journal.pone.0283469

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